Manifold Learning for Complex Visual Analytics: Benefits from and to Neural Architectures
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1 Manfold Learnng for Complex Vsual Analytcs: Benefts from and to Neural Archtectures Stephane Marchand-Mallet Vper group Unversty of Geneva Swtzerland Edgar Roman-Rangel, Ke Sun (Vper) A. Agocs, D. Dardans, R. Forster, J.-M. Le Goff, X. Ouvrard (CERN) Unversty of Geneva BoTech Geneva May
2 Outlne Vsual Analytcs and Manfold Learnng Dervng manfold Learnng strateges Spacetme Informaton geometry Make Manfold Learnng nductve wth Neural Archtectures Applcaton potental: Vsualsng Neuroscence data Unversty of Geneva BoTech Geneva May
3 Manfold learnng Fg 3. from J. B. Tenenbaum, V. de Slva, J. C. Langford, A Global Geometrc Framework for Nonlnear Dmensonalty Reducton, Scence 290, (2000), Choce of features Preservaton of local nformaton MDS : preserve exact neghborhood t-sne : preserve neghborhood dstrbuton At the heart of vsualsaton (and Vsual Analytcs) Stephane.Marchand-Mallet@unge.ch Unversty of Geneva <@> BoTech Geneva May
4 Unversty of Geneva BoTech Geneva May ) ( } { fnd } { Gven D d y x d N D N Dstance-based Stochastc neghbourhood Preservng local nformaton scale d w y y x x d y 2 ) ( mn y y h y y h q x x h x x h p Sun, K., Bruno, E., & Marchand-Mallet, S. (2012). Stochastc Unfoldng. In IEEE Machne Learnng for Sgnal Processng Workshop (MLSP'2012), Santander, Span.
5 Stochastc Unfoldng (SU) Sun, K., Bruno, E., & Marchand-Mallet, S. (2012). Stochastc Unfoldng. In IEEE Machne Learnng for Sgnal Processng Workshop (MLSP'2012), Santander, Span. Unversty of Geneva BoTech Geneva May
6 Extenson to spacetme Use relatvstc pseudo-metrc tensor for ncludng a tme (negatve) dmenson Smlar stochastc embeddng formulaton usng c 2 2 ( x, y) ( x ) ( space y x tme y ) 2 Provdes more power for representaton Sun, K., Wang, J., Kalouss, A., & Marchand-Mallet, S. (2015). Space-Tme Local Embeddngs. In Proceedngs of Advances n Neural Informaton Processng Systems 28 (NIPS 2015), Montreal, Canada, December Stephane.Marchand-Mallet@unge.ch Unversty of Geneva <@> BoTech Geneva May
7 Vsualsng Spacetme Unversty of Geneva BoTech Geneva May
8 A geometrc vew of Machne Learnng Informaton Geometry allows use to consder statstcal machne learnng as geometrc operatons (eg proectons) over statstcal manfolds Sun, K., & Marchand-Mallet, S. (2014). An Informaton Geometry of Statstcal Manfold Learnng. In Proceedngs of the Internatonal Conference on Machne Learnng (ICML 2014), Beng, Chna. Unversty of Geneva BoTech Geneva May
9 Embarkng Neural Archtectures as feature extractors We use the representaton derved nternally by Deep Learnng archtectures as nput dmensons c 5 n VGGNet fnal encodng layer from (adapted) sparse autoencoders E. Roman-Rangel & S. Marchand-Mallet. COLD: Lnearly Aggregated Convolutonal Orthogonal Descrptors. Submtted to the Int. Conference on Comp. Vson Stephane.Marchand-Mallet@unge.ch Unversty of Geneva <@> BoTech Geneva May
10 Embarkng Neural Archtectures as mappers Manfold Learnng technques are transductve No absolute mapper learnt We use Neural Archtectures to make them nductve {x } Manfold Learnng {y } (transductve) NN Inductve model Unseen {x } Resultng {y } E. Roman-Rangel & S. Marchand-Mallet. Assessng Deep Learnng Archtectures for Vsualzng Maya Heroglyphs. MCPR Stephane.Marchand-Mallet@unge.ch Unversty of Geneva <@> BoTech Geneva May
11 A Vsual Analytcs platform for Bg Data Case of Neuroscence A. Agocs, D. Dardans, R. Forster, J.-M. Le Goff, X. Ouvrard CERN 11
12 The macaque case g2 s too large for vsual percepton Communtes 172 clusters edges g 0 : drected graph of bran area nterconnectvty* (42 vertces = areas, 601 edges= nteractons) g 2 : drected graph of cortcal nteractons* (Input/Processng/Target) (9869 vertces = IPT flows, edges = common nteractons) *Data/slde: L. Négyessy, A. Fülöp/Wgner Insttute, Budapest 12
13 Constructed Reachablty Graph Bran Area Modalty Target Area Processng Area Input Area Cerebral lobe L2_path ProcessType InterLobe g 0 edges g 2 edges g 2 g 0 connectons Macaque bran network data: optmal for navgaton 13
14 g 0 graph Techncal Challenge of Usng Bg Data Analytcs 14
15 g 2 (wth Quotent graph) CS platform concepts V3 15
16 Communty_61 CS platform concepts V3 16
17 Communty_61 17
18 CS platform concepts V3 18
19 Concluson / Outlook Vsual Analytcs both nherts from and complements Machne Learnng Neural Archtectures are flexble tools to learn non-lnear processes Ther ntegraton n Learnng processes can be dverse The parallel wth understandng neurologcal processes may stll have a lot to offer Stephane.Marchand-Mallet@unge.ch Unversty of Geneva <@> BoTech Geneva May
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