Learning of manifolds

Also topological data analysis; other hip names to follow

2014-08-19 – 2017-04-05

Berger, Daniels and Yu: Manifolds in Genome search

As in – handling your high-dimensional, or graphical, data by trying to discover a low(er)-dimensional manifold that contains it. That is, inferring a hidden constraint that happens to have the form of a smooth surface of some low-ish dimension. related: Learning on manifolds

There are a million different versions of this. Multidimensional scaling seems to be the oldest.

Tangential aside: in dynamical systems we talk about creating very high dimensional Takens embedding for state space reconstruction for arbitrary nonlinear dynamics. I imagine there are some connections between learning the lower-dimensional manifold upon which lies your data, and the higher dimensional manifold in which your data’s state space is naturally expressed. But I would not be the first person to notice this, so hopefully it’s done for me somewhere?

See also kernel methods, which do regression on an implicit manifold, (how do you reconcile these, btw?) and functional regression where the manifold isn’t even necessarily low dimensional, although typically still smooth, in some sense.

See also information geometry, which doesn’t give you a manifold for your data, but a manifold in which the parametric model itself is embedded.

To look at: ISOMAP, Locally linear embedding, spectral embeddings, multidimensional scaling…

Bioinformatics is leading to some weird use of data manifolds; see for example BeDY16 for the performance implications of knowing the manifold shape for *-omics search, using compressive manifold storage based on both fractal dimension and metric entropy concepts. Also suggestive connection with fitness landscape in evolution.

Neural networks have some implicit manifolds, if you squint right. see Christopher Olahs’s visual explanation how, whose diagrams should be stolen by someone trying to explain V-C dimension.

MoSF13 argue:

Manifold learning algorithms have recently played a crucial role in unsupervised learning tasks such as clustering and nonlinear dimensionality reduction … Many such algorithms have been shown to be equivalent to Kernel PCA (KPCA) with data dependent kernels, itself equivalent to performing classical multidimensional scaling (cMDS) in a high dimensional feature space (Schölkopf et al., 1998; Williams, 2002; Bengio et al., 2004). … Recently, it has been observed that the majority of manifold learning algorithms can be expressed as a regularized loss minimization of a reconstruction matrix, followed by a singular value truncation (Neufeld et al., 2012)

Implementations

TTK

TTK

The Topology ToolKit (TTK) is an open-source library and software collection for topological data analysis in scientific visualization.

TTK can handle scalar data defined either on regular grids or triangulations, either in 2D or in 3D. It provides a substantial collection of generic, efficient and robust implementations of key algorithms in topological data analysis. It includes:

  • For scalar data: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, topological simplification;

  • For bivariate scalar data: fibers, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces;

  • For uncertain scalar data: mandatory critical points;

scikit-learn

scikit-learn implements a grab-bag of algorithms

tapkee

C++: Tapkee. Pro-tip – even without coding, tapkee does a long list of nice dimensionality reduction from the CLI, some of which are explicitly manifold learners (and the rest are matrix factorisations which is not so different)

  • Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE)
  • Neighborhood Preserving Embedding (NPE)
  • Local Tangent Space Alignment (LTSA)
  • Linear Local Tangent Space Alignment (LLTSA)
  • Hessian Locally Linear Embedding (HLLE)
  • Laplacian eigenmaps
  • Locality Preserving Projections
  • Diffusion map
  • Isomap and landmark Isomap
  • Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS)
  • Stochastic Proximity Embedding (SPE)
  • PCA and randomized PCA
  • Kernel PCA (kPCA)
  • t-SNE
  • Barnes-Hut-SNE

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Aswani, Anil, Peter Bickel, and Claire Tomlin. 2011. “Regression on Manifolds: Estimation of the Exterior Derivative.” The Annals of Statistics 39 (1): 48–81. https://doi.org/10.1214/10-AOS823.

Belkin, Mikhail, and Partha Niyogi. 2003. “Laplacian Eigenmaps for Dimensionality Reduction and Data Representation.” Neural Computation 15 (6): 1373–96. https://doi.org/10.1162/089976603321780317.

Bengio, Yoshua, Aaron Courville, and Pascal Vincent. 2013. “Representation Learning: A Review and New Perspectives.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35: 1798–1828. https://doi.org/10.1109/TPAMI.2013.50.

Berger, Bonnie, Noah M. Daniels, and Y. William Yu. 2016. “Computational Biology in the 21st Century: Scaling with Compressive Algorithms.” Communications of the ACM 59 (8): 72–80. https://doi.org/10.1145/2957324.

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Chen, Minhua, J. Silva, J. Paisley, Chunping Wang, D. Dunson, and L. Carin. 2010. “Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds.” IEEE Transactions on Signal Processing 58 (12): 6140–55. https://doi.org/10.1109/TSP.2010.2070796.

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Freund, Yoav, Sanjoy Dasgupta, Mayank Kabra, and Nakul Verma. 2007. “Learning the Structure of Manifolds Using Random Projections.” In Advances in Neural Information Processing Systems, 473–80. http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2007_133.pdf.

Gashler, Mike, and Tony Martinez. 2012. “Robust Manifold Learning with CycleCut.” Connection Science 24 (1): 57–69. https://doi.org/10.1080/09540091.2012.664122.

Hadsell, R., S. Chopra, and Y. LeCun. 2006. “Dimensionality Reduction by Learning an Invariant Mapping.” In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2:1735–42. https://doi.org/10.1109/CVPR.2006.100.

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He, Xiaofei, and Partha Niyogi. 2003. “Locality Preserving Projections.” In Proceedings of the 16th International Conference on Neural Information Processing Systems, 16:153–60. NIPS’03. Cambridge, MA, USA: MIT Press. https://papers.nips.cc/paper/2359-locality-preserving-projections.pdf.

Huckemann, Stephan F., Peter T. Kim, Ja-Yong Koo, and Axel Munk. 2010. “Möbius Deconvolution on the Hyperbolic Plane with Application to Impedance Density Estimation.” The Annals of Statistics 38 (4): 2465–98. https://doi.org/10.1214/09-AOS783.

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Lahiri, Subhaneil, Peiran Gao, and Surya Ganguli. 2016. “Random Projections of Random Manifolds,” July. http://arxiv.org/abs/1607.04331.

Maaten, Laurens van der, and Geoffrey Hinton. 2008. “Visualizing Data Using T-SNE.” Journal of Machine Learning Research 9 (Nov): 2579–2605. http://www.jmlr.org/papers/v9/vandermaaten08a.html.

Moustafa, Karim Abou-, Dale Schuurmans, and Frank Ferrie. 2013. “Learning a Metric Space for Neighbourhood Topology Estimation: Application to Manifold Learning.” In Journal of Machine Learning Research, 341–56. http://jmlr.org/proceedings/papers/v29/Moustafa13.html.

Mukherjee, Sayan, Qiang Wu, and Ding-Xuan Zhou. 2010. “Learning Gradients on Manifolds.” Bernoulli 16 (1): 181–207. https://doi.org/10.3150/09-BEJ206.

Roweis, Sam T., and Lawrence K. Saul. 2000. “Nonlinear Dimensionality Reduction by Locally Linear Embedding.” Science 290 (5500): 2323–6. https://doi.org/10.1126/science.290.5500.2323.

Saul, Lawrence K., and Sam T. Roweis. 2003. “Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds.” The Journal of Machine Learning Research 4 (December): 119–55. https://doi.org/10.1162/153244304322972667.

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Wang, Boyue, Yongli Hu, Junbin Gao, Yanfeng Sun, Haoran Chen, and Baocai Yin. 2017. “Locality Preserving Projections for Grassmann Manifold.” In PRoceedings of IJCAI, 2017. http://arxiv.org/abs/1704.08458.

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