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Archetypal Analysis for Machine Learning

Archetypal Analysis for Machine Learning

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Archetypal Analysis for Machine Learning

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  1. Archetypal Analysis for Machine Learning Morten Mørup DTU Informatics Cognitive Systems Group Technical University of Denmark Joint work with Lars Kai Hansen DTU Informatics Cognitive Systems Group Technical University of Denmark MLSP 2010 September 1st

  2. MLSP 2010 September 1st

  3. Archetypical Analysis (AA) X X C S  AA formed by two simplex constraintsArchetype: Xck formed by convex combination of the data points Projection: sn gives the convex combination of archetypes forming each data point MLSP 2010 September 1st

  4. The Original paper of Adler and Breiman considered 3 applications Swiss army head shape Los Angeles Basin air polution 1976 Tokamak Fusion Data Other Applications: Flame dynamics (Stone & Adler 1996) End member extraction of Galaxy Spectra (Chan et al, 2003) Data driven Benchmarking (Porzio et al. 2008) MLSP 2010 September 1st

  5. Archetypical analysis extract the ”principal convex hull” (PCH) of the data cloud Convex hull: Blue lines and light shaded region (dots indicate points in convex set) Dominant convex hull: green lines and gray shaded region (dots indicate archetypes) (Dwyer, 1988) While convex set can be identified in linear time O(N) (McCallum & Avis 1979)finding C and S is a non-convex (NP hard) problem. NB: One might think that AA is highy driven by outliers, however, ”outliers” are only relevant if they reflect representative dynamics in the data! MLSP 2010 September 1st

  6. Our (new) mathematical results: 1: The AA/PCH model is in general unique! See Theorem 1 2: The AA/PCH model can be efficiently initialized by the proposed FurthestSum algorithm 3: The AA/PCH model parameters can be efficiently optimized by normalization invariant projected gradient For details on derivation of updates and their computational complexity see section 2.3 The proposed FurthestSum algorithmguarantee extraction of points in the convex set, see Theorem 2 Large scale Applications MLSP 2010 September 1st

  7. Our Machine Learning Applications • Computer vision • NeuroImaging • TextMining • Collaborative Filtering MLSP 2010 September 1st

  8. Computer Vision: CBCL face database Face database: K=361 pixels, N=2429  all images belong with probabilty 1 to convex set  X X C S SVD/PCA: Low -> high freq. dynamicsNMF: Part Based RepresentationAA: Archetypes/FreaksK-means: Centroids/Prototypes MLSP 2010 September 1st

  9. Archetypal Analysis naturally bridges clustering methods with low rank representations MLSP 2010 September 1st

  10. NeuroImaging: Positron Emission Tomography  X X C S Altansering tracer injected, recorded signal in theory mixture of 3 underlying binding profiles (Archetypes): Low binding regions, High binding regions and artery/veines. Each voxel a given concentration fraction of these tissue types. XC S Low Binding High Binding Artery/Veines MLSP 2010 September 1st

  11. Text Mining: NIPS term-document (bag of words) X X C S  XC: Distinct Aspects Prototypical Aspects MLSP 2010 September 1st

  12. Collaborative filtering: MovieLens Medium size and large size Movie lens data ( Medium size: 1,000,209 ratings of 3,952 movies by 6,040 users Large size: 10,000,054 ratings of 10,677 movies given by 71,567 Extracts features representing distinct user types, each user represented as a given concentration fraction of the user types. AA appear to have less tendency to overfit. MLSP 2010 September 1st

  13. Conclusion • Archetypal Analysis is Unique in general (Theorem 1) • Archetypal Analysis can be efficiently initialized by the proposed FurhtestSum algorithm (Theorem 2) and optimized through normalization invariant projected gradient. • Archetypal Analysis naturally bridges clustering with low rank approximations • Archetypal Analysis results in easy interpretable features that are closely related to the actual data • Archetypal Analysis useful for a large variety of machine learning problem domains within unsupervised learning.(Computer Vision, NeuroImaging, TextMining, Collaborative Filtering) • Archetypal Analysis can be extended to kernel representations finding the principal convex hull in (a potentially infinite) Hilbert space (see section 2.4 of the paper). MLSP 2010 September 1st

  14. Open problems and current research directions: • What is the optimal number of components?Cross-validation based on missing value prediction (see also collaborative filtering example in the paper)Bayesian generative models for AA/PCH that automatically penalize model complexity. • What if ’pure’ archetypes cannot be well represented by the data available? vs. MLSP 2010 September 1st

  15. Selected References from the paper [1] Adele Cutler and Leo Breiman, “Archetypal analysis,” Technometrics, vol. 36, no. 4, pp. 338–347, Nov 1994. [2] D. S. Hochbaum and D. B. Shmoys., “A best possible heuristic or the k-center problem.,” Mathematics of Operational Research, vol. 10, no. 2, pp. 180–184, 1985. [7] Emily Stone and Adele Cutler, “Introduction to archetypal analysis of spatio-temporal dynamics,” Phys. D, vol. 96, no.1-4, pp. 110–131, 1996.[8] Giovanni C. Porzio, Giancarlo Ragozini, and Domenico Vistocco, “On the use of archetypes as benchmarks,” Appl. Stoch. Model. Bus. Ind., vol. 24, no. 5, pp. 419–437, 2008. [9] B. H. P. Chan, D. A. Mitchell, and L. E. Cram, “Archetypal analysis of galaxy spectra,” MON.NOT.ROY.ASTRON.SOC., vol. 338, pp. 790, 2003. [11] D. McCallum and D. Avis, “A linear algorithm for finding the convex hull of a simple polygon,” Information Processing Letters, vol. 9, pp. 201–206, 1979. [12] Rex A. Dwyer, “On the convex hull of random points in a polytope,” Journal of Applied Probability, vol. 25, no. 4, pp.688–699, 1988. MLSP 2010 September 1st