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Machine Learning Summer School 2006 - Taipei

Kernels on histograms through the transportation polytope

author: Marco Cuturi, Institute of Statistical Mathematics in Tokyo

Description

For two integral histograms and of equal sum, the Monge-Kantorovich distance MK(r,c) between r and c parameterized by a d × d cost matrix T is the minimum of all costs <F,T> taken over matrices F of the transportation polytope U(r,c). Recent results suggest that this distance is not negative definite, and hence, through Schoenberg's well-known result, may not be a positive definite kernel for all t > 0. Rather than using directly MK to define a similarity between r and c, we present in this talk kernels on r and c based on the whole transportation polytope U(r,c). We prove that when r and c have binary counts, which is equivalent to stating that r and c depict clouds of points of equal size, the permanent of their Gram matrix induced by the cost matrix T is a positive definite kernel under favorable conditions on T. We also show that the volume of the polytope U(r,c), that is the number of integral transportation plans, is a positive definite quantity in r and c through the Robinson-Schensted-Knuth correspondence between transportation matrices and Young Tableaux.

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Slides
0:00 Kernels on Histograms through the Transportation Polytope
0:11 Outline
0:47 Problem
1:21 Problem 01
1:39 Problem 02
2:10 Problem 03
2:28 Problem 04
2:58 Problem 05
3:09 Bag-of-components, histogram representations
3:36 Bag-of-components, histogram representations 01
3:45 Bag-of-components, histogram representations 02
3:59 Probability representation
4:20 Probability representation 01
4:25 d  Rd
+  Rd
4:39 d  Rd
+  Rd 01
5:01 d  Rd
+  Rd 02
5:34 d  Rd
+  Rd 03
5:58 Dozens of classic probability metrics
6:41 Dozens of classic probability metrics 01
7:12 Dozens of classic probability metrics 02
8:20 Dozens of classic probability metrics 03
8:49 Outline
8:55 MK distance between two histograms
9:17 MK distance between two histograms 01
10:00 MK distance between two histograms 02
10:29 MK distance between two histograms 03
11:04 MK distance between two histograms 04
11:25 MK distance between two histograms 05
11:38 MK distance between two histograms 06
12:22 Permanent
12:40 Permanent kernel between clouds of points
12:46 Permanent kernel between clouds of points 01
12:52 Permanent kernel between clouds of points 02
13:02 Permanent kernel between clouds of points 03
13:34 Permanent kernel between clouds of points 05
14:59 Volume of U(r , c) for general histograms
15:32 Volume of U(r , c) for general histograms 01
15:45 Volume of U(r , c) for general histograms 02
15:55 Positive definiteness of the volume
16:01 Positive definiteness of the volume 01
16:16 Positive definiteness of the volume 02
16:36 Weighted volume of U(r , c)
17:13 Weighted volume of U(r , c) 01
17:26 Positive definiteness of weighted volumes
17:47 Positive definiteness of weighted volumes 01
17:53 Positive definiteness of weighted volumes 02
18:22 2000 images, 10 classes, 20 pixels per image

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