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<!DOCTYPE html>
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<title>Overview of mvlearn — mvlearn alpha documentation</title>
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<p class="caption" role="heading"><span class="caption-text">Using mvlearn</span></p>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Overview of mvlearn</a></li>
<li class="toctree-l1"><a class="reference internal" href="install.html">Install</a><ul>
<li class="toctree-l2"><a class="reference internal" href="install.html#pip-installation-instructions">pip installation instructions</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_coregularized_spectral_tutorial.html">Multiview Coregularized Spectral Clustering Comparison</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_spherical_kmeans_tutorial.html">Multiview Spherical KMeans Tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_kmeans_tutorial.html">Multiview KMeans Tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_vs_singleview_spectral.html">Multiview vs. Singleview Spectral Clustering of UCI Multiview Digits</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_kmeans_validation_simulated.html">Multiview vs. Singleview KMeans</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_spectral_tutorial.html">Multiview Spectral Clustering Tutorial</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_spectral_validation_complex.html">Conditional Independence of Views on Multiview Spectral Clustering</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/cluster/plot_mv_kmeans_validation_complex.html">Conditional Independence of Views on Multiview KMeans Clustering</a></li>
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<li class="toctree-l2"><a class="reference internal" href="auto_examples/index.html#examples-on-compose">Examples on compose</a><ul>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/compose/plot_multiview_construction.html">Constructing multiple views to classify singleview data</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/compose/plot_pipeline_sklearn_integration.html">Integrating mvlearn with scikit-learn</a></li>
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<li class="toctree-l2"><a class="reference internal" href="auto_examples/index.html#examples-on-datasets">Examples on datasets</a><ul>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/datasets/plot_load_ucimultifeature.html">Loading and Viewing the UCI Multiple Features Dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/datasets/plot_gaussianmixtures.html">Generating Multiview Data from Gaussian Mixtures</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/datasets/plot_nutrimouse.html">An mvlearn case study: the Nutrimouse dataset</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="auto_examples/index.html#examples-on-decomposition">Examples on decomposition</a><ul>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/decomposition/plot_group_ica_tutorial.html">ICA: a tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/decomposition/plot_mv_ica_tutorial.html">Multiview Independent Component Analysis (ICA) Comparison</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/decomposition/plot_ajive_tutorial.html">Angle-based Joint and Individual Variation Explained (AJIVE)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="auto_examples/index.html#examples-on-embed">Examples on embed</a><ul>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/embed/plot_gcca_tutorial.html">Generalized Canonical Correlation Analysis (GCCA) Tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/embed/plot_mcca_tutorial.html">CCA Tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/embed/plot_dcca_tutorial.html">Deep CCA (DCCA) Tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/embed/plot_kmcca_pgso_tutorial.html">Partial Gram-Schmidt Orthogonalization (PGSO) for KMCCA</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/embed/plot_mvmds_tutorial.html">Multidimensional Scaling (MVMDS) Tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/embed/plot_cca_comparison.html">Comparing CCA Variants</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/embed/plot_kmcca_tutorial.html">Kernel MCCA (KMCCA) Tutorial</a></li>
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<li class="toctree-l2"><a class="reference internal" href="auto_examples/index.html#examples-on-plotting">Examples on plotting</a><ul>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/plotting/plot_quick_visualize_tutorial.html">Quickly Visualizing Multiview Data</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/plotting/plot_crossviews_plot.html">Plotting Multiview Data with a Cross-view Plot</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="auto_examples/index.html#examples-on-semi-supervised">Examples on semi_supervised</a><ul>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/semi_supervised/plot_cotraining_regression.html">2-View Semi-Supervised Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/semi_supervised/plot_cotraining_classification.html">2-View Semi-Supervised Classification</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="references/index.html">Reference</a><ul>
<li class="toctree-l2"><a class="reference internal" href="references/embed.html">Embedding</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#canonical-correlation-analysis-cca">Canonical Correlation Analysis (CCA)</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#multiview-canonical-correlation-analysis-mcca">Multiview Canonical Correlation Analysis (MCCA)</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#kernel-mcca">Kernel MCCA</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#generalized-canonical-correlation-analysis-gcca">Generalized Canonical Correlation Analysis (GCCA)</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#deep-canonical-correlation-analysis-dcca">Deep Canonical Correlation Analysis (DCCA)</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#multiview-multidimensional-scaling">Multiview Multidimensional Scaling</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#split-autoencoder">Split Autoencoder</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#dcca-utilities">DCCA Utilities</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/embed.html#dimension-selection">Dimension Selection</a></li>
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</li>
<li class="toctree-l2"><a class="reference internal" href="references/decomposition.html">Decomposition</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/decomposition.html#multiview-ica">Multiview ICA</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/decomposition.html#group-ica">Group ICA</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/decomposition.html#group-pca">Group PCA</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/decomposition.html#angle-based-joint-and-individual-variation-explained-ajive">Angle-Based Joint and Individual Variation Explained (AJIVE)</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="references/cluster.html">Clustering</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/cluster.html#multiview-spectral-clustering">Multiview Spectral Clustering</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/cluster.html#co-regularized-multiview-spectral-clustering">Co-Regularized Multiview Spectral Clustering</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/cluster.html#multiview-k-means">Multiview K Means</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/cluster.html#multiview-spherical-k-means">Multiview Spherical K Means</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="references/semi_supervised.html">Semi-Supervised</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/semi_supervised.html#cotraining-classifier">Cotraining Classifier</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/semi_supervised.html#cotraining-regressor">Cotraining Regressor</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="references/model_selection.html">Model Selection</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/model_selection.html#cross-validation">Cross Validation</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/model_selection.html#train-test-split">Train-Test Split</a></li>
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<li class="toctree-l2"><a class="reference internal" href="references/compose.html">Compose</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#averagemerger">AverageMerger</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#concatmerger">ConcatMerger</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#randomgaussianprojection">RandomGaussianProjection</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#randomsubspacemethod">RandomSubspaceMethod</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#simplesplitter">SimpleSplitter</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#viewclassifier">ViewClassifier</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/compose.html#viewtransformer">ViewTransformer</a></li>
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<li class="toctree-l2"><a class="reference internal" href="references/datasets.html">Multiview Datasets</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/datasets.html#uci-multiple-feature-dataset">UCI multiple feature dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/datasets.html#nutrimouse-dataset">Nutrimouse dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/datasets.html#data-simulator">Data Simulator</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/datasets.html#factor-model">Factor Model</a></li>
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<li class="toctree-l3"><a class="reference internal" href="references/plotting.html#quick-visualize">Quick Visualize</a></li>
<li class="toctree-l3"><a class="reference internal" href="references/plotting.html#crossviews-plot">Crossviews Plot</a></li>
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<li class="toctree-l2"><a class="reference internal" href="references/utils.html">Utility Functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="references/utils.html#io">IO</a></li>
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<p class="caption" role="heading"><span class="caption-text">Developer Information</span></p>
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<li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing to mvlearn</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="contributing.html#how-to-make-a-good-bug-report">How to make a good bug report</a></li>
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<li class="toctree-l2"><a class="reference internal" href="contributing.html#contributing-code">Contributing Code</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="contributing.html#guidelines">Guidelines</a><ul>
<li class="toctree-l3"><a class="reference internal" href="contributing.html#coding-guidelines">Coding Guidelines</a></li>
<li class="toctree-l3"><a class="reference internal" href="contributing.html#docstring-guidelines">Docstring Guidelines</a></li>
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<li class="toctree-l2"><a class="reference internal" href="contributing.html#api-of-mvlearn-objects">API of mvlearn Objects</a><ul>
<li class="toctree-l3"><a class="reference internal" href="contributing.html#estimators">Estimators</a></li>
<li class="toctree-l3"><a class="reference internal" href="contributing.html#additional-functionality">Additional Functionality</a></li>
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<li class="toctree-l1"><a class="reference internal" href="changelog.html">Changelog</a><ul>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#version-0-5-0">Version 0.5.0</a></li>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#version-0-4-1">Version 0.4.1</a></li>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#version-0-4-0">Version 0.4.0</a><ul>
<li class="toctree-l3"><a class="reference internal" href="changelog.html#id5">mvlearn.compose</a></li>
<li class="toctree-l3"><a class="reference internal" href="changelog.html#id13">mvlearn.construct</a></li>
<li class="toctree-l3"><a class="reference internal" href="changelog.html#id15">mvlearn.decomposition</a></li>
<li class="toctree-l3"><a class="reference internal" href="changelog.html#id17">mvlearn.embed</a></li>
<li class="toctree-l3"><a class="reference internal" href="changelog.html#id21">mvlearn.model_selection</a></li>
<li class="toctree-l3"><a class="reference internal" href="changelog.html#id24">mvlearn.utils</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#version-0-3-0">Version 0.3.0</a></li>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#patch-0-2-1">Patch 0.2.1</a></li>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#version-0-2-0">Version 0.2.0</a></li>
<li class="toctree-l2"><a class="reference internal" href="changelog.html#version-0-1-0">Version 0.1.0</a></li>
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<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
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<p class="caption" role="heading"><span class="caption-text">Useful Links</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://github.com/mvlearn/mvlearn">mvlearn @ GitHub</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pypi.org/project/mvlearn/">mvlearn @ PyPI</a></li>
<li class="toctree-l1"><a class="reference external" href="https://github.com/mvlearn/mvlearn/issues">Issue Tracker</a></li>
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<section id="overview-of-mvlearn">
<h1>Overview of <a class="reference external" href="https://github.com/mvlearn/mvlearn">mvlearn</a><a class="headerlink" href="#overview-of-mvlearn" title="Permalink to this headline">¶</a></h1>
<p>mvlearn is a Python module for machine learning on multiview data (sometimes referred to as multi-modal, multi-table, or multi-block data).</p>
</section>
<section id="documentation">
<h1>Documentation<a class="headerlink" href="#documentation" title="Permalink to this headline">¶</a></h1>
<div class="toctree-wrapper compound">
<p class="caption" role="heading"><span class="caption-text">Using mvlearn</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="current reference internal" href="#">Overview of mvlearn</a></li>
<li class="toctree-l1"><a class="reference internal" href="install.html">Install</a></li>
<li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html">Examples Gallery</a></li>
<li class="toctree-l1"><a class="reference internal" href="references/index.html">Reference</a></li>
</ul>
</div>
<div class="toctree-wrapper compound">
<p class="caption" role="heading"><span class="caption-text">Developer Information</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing to mvlearn</a></li>
<li class="toctree-l1"><a class="reference internal" href="changelog.html">Changelog</a></li>
<li class="toctree-l1"><a class="reference internal" href="license.html">License</a></li>
</ul>
</div>
<div class="toctree-wrapper compound">
<p class="caption" role="heading"><span class="caption-text">Useful Links</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://github.com/mvlearn/mvlearn">mvlearn @ GitHub</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pypi.org/project/mvlearn/">mvlearn @ PyPI</a></li>
<li class="toctree-l1"><a class="reference external" href="https://github.com/mvlearn/mvlearn/issues">Issue Tracker</a></li>
</ul>
</div>
<section id="motivation">
<h2>Motivation<a class="headerlink" href="#motivation" title="Permalink to this headline">¶</a></h2>
<p>mvlearn aims to serve as a community-driven open-source software package that offers reference implementations for algorithms
and methods related to multiview learning, machine learning in settings where there are multiple incommensurate views or feature
sets for each sample. It brings together the most widely-used tools in this setting with a standardized scikit-learn like API,
well tested code and high-quality documentation. Doing so we aim to facilitate application, extension, comparison of methods, and
offer a foundation for research into new multiview algorithms. We welcome new contributors and the addition of methods with proven
efficacy and current use.</p>
</section>
<section id="background">
<h2>Background<a class="headerlink" href="#background" title="Permalink to this headline">¶</a></h2>
<p>Multiview data, in which each sample is represented by multiple views of distinct features, are often seen in real-world data,
and related methods have grown in popularity. A view is defined as a partition of the complete set of feature variables
<a class="footnote-reference brackets" href="#xu-2013" id="id2">1</a>. Depending on the domain, these views may arise naturally from unique sources, or they may correspond to
subsets of the same underlying feature space. For example, a doctor may have an MRI scan, a CT scan, and the answers to a clinical
questionnaire for a diseased patient. However, classical methods for inference and analysis are often poorly suited to account for
multiple views of the same sample, since they cannot properly account for complementing views that hold differing statistical
properties <a class="footnote-reference brackets" href="#zhao-2017" id="id3">2</a>. To deal with this, many multiview learning methods have been developed to take advantage of multiple
data views and produce better results in various tasks <a class="footnote-reference brackets" href="#sun-2013" id="id4">3</a> <a class="footnote-reference brackets" href="#hardoon-2004" id="id5">4</a> <a class="footnote-reference brackets" href="#chao-2017" id="id6">5</a> <a class="footnote-reference brackets" href="#yang-2014" id="id7">6</a>.</p>
</section>
<section id="examples">
<h2>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h2>
<section id="brief-examples">
<h3>Brief examples<a class="headerlink" href="#brief-examples" title="Permalink to this headline">¶</a></h3>
<ul>
<li><p>Import mvlearn</p>
<blockquote>
<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mvlearn</span>
</pre></div>
</div>
</div></blockquote>
</li>
<li><p>Decompose two views using multiview PCA to capture joint information</p>
<blockquote>
<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mvlearn.decomposition</span> <span class="kn">import</span> <span class="n">GroupPCA</span>
<span class="c1"># X1 and X2 are data matrices, each with n samples</span>
<span class="n">Xs</span> <span class="o">=</span> <span class="p">[</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">]</span> <span class="c1"># multiview data</span>
<span class="n">Xs_components</span> <span class="o">=</span> <span class="n">GroupPCA</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">Xs</span><span class="p">)</span>
</pre></div>
</div>
</div></blockquote>
</li>
<li><p>Cluster two views using multiview KMeans to find shared labels</p>
<blockquote>
<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mvlearn.cluster</span> <span class="kn">import</span> <span class="n">MultiviewKMeans</span>
<span class="c1"># X1 and X2 are data matrices, each with n samples</span>
<span class="n">Xs</span> <span class="o">=</span> <span class="p">[</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">]</span> <span class="c1"># multiview data</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">MultiviewKMeans</span><span class="p">()</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">Xs</span><span class="p">)</span>
</pre></div>
</div>
</div></blockquote>
</li>
</ul>
</section>
<section id="highlighted-full-examples">
<h3>Highlighted full examples<a class="headerlink" href="#highlighted-full-examples" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><dl class="simple">
<dt><a class="reference external" href="auto_examples/datasets/plot_nutrimouse.html">Nutrimouse dataset case study</a>:</dt><dd><p>A collection of multiview learning methods across modules provide insights to a 2-view genomics dataset.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt><a class="reference external" href="auto_examples/cluster/plot_mv_vs_singleview_spectral.html">Multiview vs singleview clustering on the UCI multiview digits</a>:</dt><dd><p>Multiview clustering strongly outperforms single view clustering on a multiview dataset of handwritten digits.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt><a class="reference external" href="auto_examples/embed/plot_cca_comparison.html">A comparison of CCA algorithms</a>:</dt><dd><p>Canonical correlation analysis (CCA) variants find linearly correlated projections of each view. Linear and nonlinear
variants are compared in various simulated settings.</p>
</dd>
</dl>
</li>
</ul>
</section>
</section>
<section id="python">
<h2>Python<a class="headerlink" href="#python" title="Permalink to this headline">¶</a></h2>
<p>Python is a powerful programming language that allows concise expressions of network
algorithms. Python has a vibrant and growing ecosystem of packages that
mvlearn uses to provide more features such as numerical linear algebra. In order to make the most out of mvlearn you will want to know how
to write basic programs in Python. Among the many guides to Python, we
recommend the <a class="reference external" href="https://docs.python.org/3/">Python documentation</a>.</p>
<p>Currently, mvlearn is supported for Python 3.6, 3.7, and 3.8.</p>
</section>
<section id="free-software">
<h2>Free software<a class="headerlink" href="#free-software" title="Permalink to this headline">¶</a></h2>
<p>mvlearn is free software; you can redistribute it and/or modify it under the
terms of the <a class="reference internal" href="license.html"><span class="doc">MIT License</span></a>. We welcome contributions.
Join us on <a class="reference external" href="https://github.com/mvlearn/mvlearn">GitHub</a>.</p>
</section>
<section id="history">
<h2>History<a class="headerlink" href="#history" title="Permalink to this headline">¶</a></h2>
<p>mvlearn was developed during the end of 2019 by Richard Guo, Ronan Perry, Gavin Mischler, Theo Lee, Alexander Chang, Arman Koul, and Cameron Franz, a team out of the Johns Hopkins University NeuroData group.</p>
</section>
<section id="citing-mvlearn">
<h2>Citing <cite>mvlearn</cite><a class="headerlink" href="#citing-mvlearn" title="Permalink to this headline">¶</a></h2>
<p>If you find the package useful for your research, please cite our <a class="reference external" href="https://www.jmlr.org/papers/volume22/20-1370/20-1370.pdf">JMLR Paper</a>.</p>
<p>Perry, Ronan, et al. "mvlearn: Multiview Machine Learning in Python." Journal of Machine Learning Research 22.109 (2021): 1-7.</p>
<p>BibTeX entry:</p>
<div class="highlight-tex notranslate"><div class="highlight"><pre><span></span>@article<span class="nb">{</span>perry2021mvlearn,
title=<span class="nb">{</span>mvlearn: Multiview Machine Learning in Python<span class="nb">}</span>,
author=<span class="nb">{</span>Perry, Ronan and Mischler, Gavin and Guo, Richard and Lee, Theodore and Chang, Alexander and Koul, Arman and Franz, Cameron and Richard, Hugo and Carmichael, Iain and Ablin, Pierre and Gramfort, Alexandre and Vogelstein, Joshua T.<span class="nb">}</span>,
journal=<span class="nb">{</span>Journal of Machine Learning Research<span class="nb">}</span>,
volume=<span class="nb">{</span>22<span class="nb">}</span>,
number=<span class="nb">{</span>109<span class="nb">}</span>,
pages=<span class="nb">{</span>1-7<span class="nb">}</span>,
year=<span class="nb">{</span>2021<span class="nb">}</span>
<span class="nb">}</span>
</pre></div>
</div>
</section>
<section id="references">
<h2>References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
<dl class="footnote brackets">
<dt class="label" id="xu-2013"><span class="brackets"><a class="fn-backref" href="#id2">1</a></span></dt>
<dd><p>Chang Xu, Dacheng Tao, and Chao Xu. "A survey on multi-view learning."
arXiv preprint, arXiv:1304.5634, 2013.</p>
</dd>
<dt class="label" id="zhao-2017"><span class="brackets"><a class="fn-backref" href="#id3">2</a></span></dt>
<dd><p>Jing Zhao, Xijiong Xie, Xin Xu, and Shiliang Sun. "Multi-view learning overview: Recent progress and new challenges."
Information Fusion, 38:43 – 54, 2017.</p>
</dd>
<dt class="label" id="sun-2013"><span class="brackets"><a class="fn-backref" href="#id4">3</a></span></dt>
<dd><p>Shiliang Sun. "A survey of multi-view machine learning." Neural Computing and Applications, 23(7-8):2031–2038, 2013.</p>
</dd>
<dt class="label" id="hardoon-2004"><span class="brackets"><a class="fn-backref" href="#id5">4</a></span></dt>
<dd><p>David R Hardoon, Sandor Szedmak, and John Shawe-Taylor. "Canonical correlation analysis:An overview with application to learning methods."
Neural Computation, 16(12):2639–2664, 2004.</p>
</dd>
<dt class="label" id="chao-2017"><span class="brackets"><a class="fn-backref" href="#id6">5</a></span></dt>
<dd><p>Guoqing Chao, Shiliang Sun, and J. Bi. "A survey on multi-view clustering."
arXiv preprint, arXiv:1712.06246, 2017.</p>
</dd>
<dt class="label" id="yang-2014"><span class="brackets"><a class="fn-backref" href="#id7">6</a></span></dt>
<dd><p>Yuhao Yang, Chao Lan, Xiaoli Li, Bo Luo, and Jun Huan. "Automatic social circle detectionusing multi-view clustering."
In Proceedings of the 23rd ACM International Conferenceon Conference on Information and Knowledge Management, pages 1019–1028, 2014.</p>
</dd>
</dl>
</section>
</section>
<section id="indices-and-tables">
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
<ul class="simple">
<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
</ul>
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