UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

This talk will present a new approach to dimension reduction called UMAP. UMAP is grounded in manifold learning and topology, making an effort to preserve the topological structure of the data. The resulting algorithm can provide both 2D visualizations of data of comparable quality to t-SNE, and general purpose dimension reduction. UMAP has been implemented as a (scikit-learn compatible) python library that can perform efficient dimension reduction, scaling out to much larger datasets than t-SNE or other comparable algorithms (see http://github.com/lmcinnes/umap).

Transcript

1. UMAP Uniform Manifold Approximation and Projection for dimension reduction
2. Who am I? I am a research mathematician at the Tutte Institute for Mathematics and Computing My Ph.D. was in Profinite Lie Rings (no, you don’t care) I now work on applying topological techniques to unsupervised learning problems
3. What is Dimension Reduction?
4. Find the “latent” features in your data
7. Matrix Factorization Neighbour Graphs
8. Matrix Factorization Principal Component Analysis Non-negative Matrix Factorization Latent Dirichlet Allocation Word2Vec GloVe Generalised Low Rank Models Linear Autoencoder
9. Neighbour Graphs Locally Linear Embedding Laplacian Eigenmaps Hessian Eigenmaps Local Tangent Space Alignment t-SNE UMAP Isomap JSE
10. PCA is the prototypical matrix factorization
11. PCA on MNIST digits
12. PCA on Fashion MNIST
13. t-SNE is the current state-of-the art for neighbour graphs
14. t-SNE on MNIST digits
15. t-SNE on Fashion MNIST
16. Uniform Manifold Approximation and Projection
17. UMAP builds mathematical theory to justify the graph based approach
18. First, a little bit of topological data analysis…
19. Simplices
21. Theorem 1 (Nerve theorem). Let U = {Ui }i2I be a cover of a topological space X. If, for all ⇢ I T i2 Ui is either contractible or empty, then N(U) is homtopically equivalent to X. <latexit sha1_base64="8ITSuq3xcb28tfscSBtUyYXmYf8=">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</latexit> <latexit sha1_base64="8ITSuq3xcb28tfscSBtUyYXmYf8=">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</latexit> <latexit sha1_base64="8ITSuq3xcb28tfscSBtUyYXmYf8=">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</latexit> <latexit sha1_base64="8ITSuq3xcb28tfscSBtUyYXmYf8=">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</latexit>
25. If the data is uniformly distributed on the manifold then the cover will be “good”
27. When is data that nicely behaved?
28. Assumption: Data is uniformly distributed on the manifold
29. Define a Riemannian metric on the manifold to make this assumption true
32. Why choose a fixed radius? Why not have a fuzzy cover?
33. Theorem 2 (UMAP Adjunction). The functors FinReal : sFuzz ! FinEPMet and FinSing : FinEPMet ! sFuzz form an adjunction FinReal a FinSing. <latexit sha1_base64="14RNK4O3bzzPAFIzAUCYYtOueE8=">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</latexit> <latexit sha1_base64="14RNK4O3bzzPAFIzAUCYYtOueE8=">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</latexit> <latexit sha1_base64="14RNK4O3bzzPAFIzAUCYYtOueE8=">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</latexit> <latexit sha1_base64="14RNK4O3bzzPAFIzAUCYYtOueE8=">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</latexit>
35. Assumption: The manifold is locally connected
40. But our local metrics are all incompatible!
43. Theorem 2 (UMAP Adjunction). The functors FinReal : sFuzz ! FinEPMet and FinSing : FinEPMet ! sFuzz form an adjunction FinReal a FinSing. <latexit sha1_base64="14RNK4O3bzzPAFIzAUCYYtOueE8=">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</latexit> <latexit sha1_base64="14RNK4O3bzzPAFIzAUCYYtOueE8=">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</latexit> <latexit sha1_base64="14RNK4O3bzzPAFIzAUCYYtOueE8=">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</latexit> <latexit sha1_base64="14RNK4O3bzzPAFIzAUCYYtOueE8=">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</latexit>
44. f(↵, ) = ↵ + ↵ · <latexit sha1_base64="iZE9o2NLLCVem811EJWhe5r3D70=">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</latexit> <latexit sha1_base64="iZE9o2NLLCVem811EJWhe5r3D70=">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</latexit> <latexit sha1_base64="iZE9o2NLLCVem811EJWhe5r3D70=">AAAC0XicfZFNbxMxEIad5auErxSOXCxSpCIg2k2ApgekSiDEBVFo0lbKRpHXO5u16rVX9iwlslYCrvwHfg1XuPNvcL4kWgEjWX71jEcznjcppbAYhr8awYWLly5f2bjavHb9xs1brc3bh1ZXhsOQa6nNccIsSKFgiAIlHJcGWJFIOEpOXszzRx/AWKHVAGcljAs2VSITnKFHk1Z/K9uOmSxz9ojGCSB7QJ/TJaAPl4Q+XoOYpxqXcGvSaoedqLv7JOpSL/phb3cuelFvp/uURp1wEW2yiv3JZuNznGpeFaCQS2btKApLHDtmUHAJdTOuLJSMn7ApjLxUrAA7dosv1vS+JynNtPFHIV3QPyscK6ydFYl/WTDM7fncHP4tN6ow64+dUGWFoPiyUVZJiprO90VTYYCjnHnBuBF+VspzZhhHv9XzXTAv/D8UnGIO2kDhVnftBivRjC14k9QUcxcjfMRTkfrJ3DM+z70EvxkDb/yUb0swDLVx8Suh3gOTtYsX02duDf5TcCDU9EzBAtTetLUz9N/isNuJvKXvuu29wcq+DXKX3CPbJCI7ZI+8JvtkSDj5Rr6TH+RncBDMgk/Bl+XToLGquUPORPD1N7yn5b4=</latexit> <latexit sha1_base64="iZE9o2NLLCVem811EJWhe5r3D70=">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</latexit> Under a probabilistic fuzzy union the combination of weights on edges is given by
46. Suppose we were given a low dimensional representation
47. We can apply the same process to get a fuzzy graph!
48. Except we know the manifold, and don’t know the “correct” nearest neighbour distance
49. Now measure the distance between the graphs using cross- entropy and optimize
50. X a2A µ(a) log ✓ µ(a) ⌫(a) ◆ + (1 µ(a)) log ✓ 1 µ(a) 1 ⌫(a) ◆ <latexit sha1_base64="u7fUXwg3iBtccLqdNJCqNBZ5RiA=">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</latexit> <latexit sha1_base64="u7fUXwg3iBtccLqdNJCqNBZ5RiA=">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</latexit> <latexit sha1_base64="u7fUXwg3iBtccLqdNJCqNBZ5RiA=">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</latexit> <latexit sha1_base64="u7fUXwg3iBtccLqdNJCqNBZ5RiA=">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</latexit>
51. We are just embedding the graph
52. X a2A µ(a) log ✓ µ(a) ⌫(a) ◆ + (1 µ(a)) log ✓ 1 µ(a) 1 ⌫(a) ◆ <latexit sha1_base64="u7fUXwg3iBtccLqdNJCqNBZ5RiA=">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</latexit> <latexit sha1_base64="u7fUXwg3iBtccLqdNJCqNBZ5RiA=">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</latexit> <latexit sha1_base64="u7fUXwg3iBtccLqdNJCqNBZ5RiA=">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</latexit> <latexit sha1_base64="u7fUXwg3iBtccLqdNJCqNBZ5RiA=">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</latexit> Get the clumps right Get the gaps right
56. On real data?
57. UMAP on MNIST digits
58. UMAP on Fashion MNIST
59. Implementation
60. Need to find (approximate) nearest neighbours very efficiently Even in high dimensional space
61. RP-trees + NN-descent Dasgupta + Freund 2008 Dong, Charikar + Li 2011
62. Need to optimize the layout subquadratically
63. SGD + negative sampling Mikolov et al 2013 Tang et al 2016
64. Need to be high level but still fast
65. +
66. Numba is awesome!
67. • High performance • Clean code • Custom distance metrics
68. Performance Comparison t-SNE UMAP COIL20 20 seconds 7 seconds MNIST 22 minutes 98 seconds Fashion MNIST 15 minutes 78 seconds GoogleNews 4.5 hours 14 minutes UMAP speed up over t-SNE COIL20 3x MNIST 13x Fashion MNIST 11x GoogleNews 19x
69. Where Next?
70. Given the mathematical foundation, a number of options are available
71. Embed new unseen points into an existing embedding
73. Make use of labels for supervised dimension reduction
75. And combine those for metric learning
77. Derived from Adam Bielski’s Siamese/Triplet repository: https://github.com/adambielski/siamese-triplet
78. Adding one categorical variable is no harder theoretically than adding many
79. Combine spaces with different metrics Continuous, categorical, ordinal, Haversine, Levenstein, and more …
80. As long as you can provide a metric for the datatype UMAP can combine it with other datatypes!
81. UMAP for pandas dataframes!
82. https://github.com/lmcinnes/umap conda install -c conda-forge umap-learn pip install umap-learn leland.mcinnes@gmail.com @leland_mcinnes