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15-826: Multimedia Databases and Data Mining

Learn how Independent Component Analysis (ICA) can reveal hidden variables in multimedia databases and data mining. Discover patterns in high-dimensional data using ICA and PCA techniques. Explore real-world applications and implications of ICA in various domains.

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15-826: Multimedia Databases and Data Mining

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  1. 15-826: Multimedia Databasesand Data Mining Lecture #22: Independent Component Analysis (ICA) Jia-Yu Pan and Christos Faloutsos (c) C. Faloutsos and J-Y Pan (2017)

  2. Must-read Material • AutoSplit: Fast and Scalable Discovery of Hidden Variables in Stream and Multimedia Databases, Jia-Yu Pan, Hiroyuki Kitagawa, Christos Faloutsos and Masafumi Hamamoto, PAKDD 2004, Sydney, Australia (c) C. Faloutsos and J-Y Pan (2017)

  3. Outline • Motivation • Formulation • PCA and ICA • Example applications • Conclusion (c) C. Faloutsos and J-Y Pan (2017)

  4. Motivation: (Q1) Find patterns in data • Motion capture data: broad jumps Left Knee Energy exerted Take-off Right Knee Landing Energy exerted (c) C. Faloutsos and J-Y Pan (2017)

  5. Human would say Pattern 1: along diagonal Pattern 2: along vertical axis How to find these automatically? Take-off R:L=60:1 Landing R:L=1:1 Motivation: (Q1) Find patterns in data Each point is the measurement at a time tick (total 550 points). (c) C. Faloutsos and J-Y Pan (2017)

  6. Motivation: (Q2) Find hidden variables Hidden variables (=‘topics’ = concepts) Stock prices Alcoa American Express “General trend” Boeing … Citi Group “Internet bubble” (c) C. Faloutsos and J-Y Pan (2017)

  7. 0.94 0.64 0.63 0.03 Motivation:(Q2) Find hidden variables Caterpillar Intel “Internet bubble” “General trend” Hidden variables (c) C. Faloutsos and J-Y Pan (2017)

  8. Motivation:(Q2) Find hidden variables Caterpillar Intel ? B1,CAT B2,INTC B1,INTC B2,CAT ? ? Hidden variable 2 Hidden variable 1 (c) C. Faloutsos and J-Y Pan (2017)

  9. Motivation:Find hidden variables • There are two sound sources in a cocktail party… =“blind source separation” (= we don’t know the sources, nor their mixing) (c) C. Faloutsos and J-Y Pan (2017)

  10. Outline • Motivation • Formulation • PCA and ICA • Example applications • Conclusion (c) C. Faloutsos and J-Y Pan (2017)

  11. Formulation: Finding patterns Given n data points, each with m attributes. Find patterns that describe data properties the best. (c) C. Faloutsos and J-Y Pan (2017)

  12. Linear representation • Find vectors that describe the data set the best. • Each point: linear combination of the vectors (patterns): (c) C. Faloutsos and J-Y Pan (2017)

  13. Patterns as data “vocabulary” Good pattern ≈ sparse coding Only b1 is needed to describe xi. (Q) Given data xi’s, compute hi,j’s and bi’s that are “sparse”? (c) C. Faloutsos and J-Y Pan (2017)

  14. Left Right ? ? Data matrix Hidden variables Basis vectors Patterns in motion capture data Sparse ~ non-Gaussian ~ “Independent” b2 b1 n=550 ticks “Independent”: e.g., minimize mutual information. (c) C. Faloutsos and J-Y Pan (2017)

  15. ? ? Data matrix Hidden variables Basis vectors Patterns in motion capture data Sparse ~ non-Gaussian ~ “Independent” X = (U L ) VT (c) C. Faloutsos and J-Y Pan (2017)

  16. Outline • Motivation • Formulation • PCA and ICA • Example applications • Find topics in documents • Hidden variables in stock prices • Conclusion (c) C. Faloutsos and J-Y Pan (2017)

  17. Pattern discovery with ICA: AutoSplit [PAKDD 04][WIRI 05] (Q) Different modalities Video frames … Step 1: Data points (matrix) or Stock prices Step 2: Compute patterns (Q) What pattern? or … Step 3: Interpret patterns (Q) How? Text documents … Data mining (Case studies) (c) C. Faloutsos and J-Y Pan (2017)

  18. details Dimensionality reduction Finding patterns in high-dimensional data PCA finds the hyperplane. ICA finds the correct patterns. (c) C. Faloutsos and J-Y Pan (2017)

  19. Outline • Motivation • Formulation • PCA and ICA • Example applications • Find topics in documents • Hidden variables in stock prices • Visual vocabulary for retinal images • Conclusion (c) C. Faloutsos and J-Y Pan (2017)

  20. Topic discovery on text streams • Data: CNN headline news (Jan.-Jun. 1998) • Documents of 10 topics in one single text stream • Documents are sorted by date/time • Subsequent documents may have different topics … Topic 3 Topic 1 Topic 1 Date/Time … (c) C. Faloutsos and J-Y Pan (2017)

  21. Topic discovery on text streams • Data: CNN headline news (Jan.-Jun. 1998) • Documents of 10 topics in one single text stream • FIND: the document boundaries • AND: the terms of each topic Date/Time … (c) C. Faloutsos and J-Y Pan (2017) ??

  22. Topic discovery on text streams • Known: number of topics = 10 • Unknown: (1) topic of each document (2) topic description … Topic 3 Topic 1 Topic 1 Date/Time (c) C. Faloutsos and J-Y Pan (2017)

  23. Step 2 Step 3 aaron animal zoo X[nxm’] = H[nxm’] B[m’xm’] b’i = [0, 0.7, …, 0.6] • Find hyperplane (m’=10) • Find patterns B’[10x3887] Topic discovery in documents Step 1 aaron zoo xi = [1, 5, …, 0] … Windowing (n=1659) X[nxm] New stories (30 words) m=3887 (dictionary size) (Q) What does b’i mean? (c) C. Faloutsos and J-Y Pan (2017)

  24. Step 3: Interpret the patterns aaron animal zoo Top words: “animal”, “zoo”, … b’i = [0, 0.7, …, 0.6] A hidden topic! m=3887 (dictionary size) Topics found General idea: related to the data attributes (c) C. Faloutsos and J-Y Pan (2017)

  25. Step 3: Evaluate the patterns AutoSplit finds correct topics. (c) C. Faloutsos and J-Y Pan (2017)

  26. Step 3: Evaluate the patterns AutoSplit’s topics are better than PCA. (c) C. Faloutsos and J-Y Pan (2017)

  27. Step 3: Evaluate the patterns Topic 1 Topic 2 PCA vectors mix the topics. AutoSplit’s topics are better than PCA. (c) C. Faloutsos and J-Y Pan (2017)

  28. Outline • Motivation • Formulation • PCA and ICA • Example applications • Find topics in documents • Hidden variables in stock prices • Conclusion (c) C. Faloutsos and J-Y Pan (2017)

  29. Find hidden variables (DJIA stocks) • Weekly DJIA closing prices • 01/02/1990-08/05/2002, n=660 data points • A data point: prices of 29 companies at the time Alcoa American Express Boeing Caterpillar Citi Group (c) C. Faloutsos and J-Y Pan (2017)

  30. Formulation: Find hidden variables AA1, …, XOM1 … … AAn, …, XOMn H11, H12, …, H1m … … Hn1, Hn2, …, Hnm B11, B12, …, B1m … Bm1, Bm2, …, Bmm ? = ? Date Hidden variable Date (c) C. Faloutsos and J-Y Pan (2017)

  31. Characterize hidden variable by the companies it influences Caterpillar Intel B1,CAT 0.94 0.64 B2,INTC 0.63 0.03 B1,INTC B2,CAT “Internet bubble” “General trend” (c) C. Faloutsos and J-Y Pan (2017)

  32. Companies related to hidden variable 1 “General trend” (c) C. Faloutsos and J-Y Pan (2017)

  33. Companies related to hidden variable 1 All companies are affected by the “general trend” variable (with weights 0.6~0.9), except AT&T. (c) C. Faloutsos and J-Y Pan (2017)

  34. General trend (and outlier) “General trend” AT&T United Technologies Walmart Exxon Mobil (c) C. Faloutsos and J-Y Pan (2017)

  35. Companies related to hidden variable 2 Techcompany 2000-2001 “Internet bubble” (c) C. Faloutsos and J-Y Pan (2017)

  36. Companies related to hidden variable 2 Tech company Companies affected by the “internet bubble” variable (with weights 0.5~0.6) are tech-related. Other companies are un-related (weights < 0.15). (c) C. Faloutsos and J-Y Pan (2017)

  37. Outline • Motivation • Formulation • PCA and ICA • Example applications • Find topics in documents • Hidden variables in stock prices • Visual vocabulary for retinal images • Conclusion (c) C. Faloutsos and J-Y Pan (2017)

  38. Conclusion • ICA: more flexible than PCA in finding patterns. • Many applications • Find topics and “vocabulary” for images • Find hidden variables in time series (e.g., stock prices) • Blind source separation • Rule of thumb: plot after PCA; • if ‘chicken-feet’, try ICA ICA PCA (c) C. Faloutsos and J-Y Pan (2017)

  39. Citation • AutoSplit: Fast and Scalable Discovery of Hidden Variables in Stream and Multimedia Databases, Jia-Yu Pan, Hiroyuki Kitagawa, Christos Faloutsos and Masafumi Hamamoto PAKDD 2004, Sydney, Australia (c) C. Faloutsos and J-Y Pan (2017)

  40. References • Jia-Yu Pan, Andre Guilherme Ribeiro Balan, Eric P. Xing, Agma Juci Machado Traina, and Christos Faloutsos. Automatic Mining of Fruit Fly Embryo Images. In Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2006. • Arnab Bhattacharya, Vebjorn Ljosa, Jia-Yu Pan, Mark R. Verardo, Hyungjeong Yang, Christos Faloutsos, and Ambuj K. Singh. ViVo: Visual Vocabulary Construction for Mining Biomedical Images. In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM), 2005. • Masafumi Hamamoto, Hiroyuki Kitagawa, Jia-Yu Pan, and Christos Faloutsos. A Comparative Study of Feature Vector-Based Topic Detection Schemes for Text Streams. In Proceedings of International Workshop on Challenges in Web Information Retrieval and Integration (WIRI), 2005, pp.125-130. • Jia-Yu Pan, Hiroyuki Kitagawa, Christos Faloutsos, and Masafumi Hamamoto. AutoSplit: Fast and Scalable Discovery of Hidden Variables in Stream and Multimedia Databases. In Proceedings of the The Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2004. (c) C. Faloutsos and J-Y Pan (2017)

  41. References • Aapo Hyvärinen, Juha Karhunen, Erkki Oja: Independent Component Analysis, John Wiley & Sons, 2001 (c) C. Faloutsos and J-Y Pan (2017)

  42. Software • Open source software: ‘fastICA’http://research.ics.tkk.fi/ica/fastica/ • Or ‘autosplit’: www.cs.cmu.edu/~jypan/software/autosplit_cmu.tar.gz (c) C. Faloutsos and J-Y Pan (2017)

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