Kernels on histograms through the transportation polytope
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.
| 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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