en
0.25
0.5
0.75
1.25
1.5
1.75
2
Machine Learning on Distributions
Published on Jan 16, 20134502 Views
Low-dimensional embedding, manifold learning, clustering, classification, and anomaly detection are among the most important problems in machine learning. The existing methods usually consider the
Related categories
Chapter list
Machine Learning on Distributions00:00
Joint work with…00:02
GOAL: Machine Learning on Distributions00:08
Outline02:26
Divergence estimation03:03
The Estimator05:17
Does it make sense?06:52
Asymptotically Unbiased07:52
A little problem…10:49
Be careful, some mistakes are easy to make…12:40
2nd Contribution ML on distributions13:37
Machine Learning on Distributions13:44
Distribution Classification14:17
Support Distribution Machine15:15
Kernel Estimation16:33
3rd Contribution Applications19:53
Image Representation with Distributions20:07
Detecting Anomalous Images (1)21:57
Detecting Anomalous Images (2)22:50
GMM-5 Density Approximation23:14
Noisy USPS Dataset Classification24:00
Multidimensional Scaling of USPS Data25:36
Local Linear Embedding of Distributions26:18
Object Classification (1)27:32
Object Classification (2)28:34
Outdoor Scenes Classification29:00
Sport Events Classification29:35
Finding Unusual Galaxy Clusters30:32
Understanding Turbulences32:49
Turbulence Data Classification33:36
Finding Vortices34:58
Find Interesting Phenomena in Turbulence Data35:21
4th Contribution Theory35:59
So far, we got good experimental results in applications36:05
Standard, finite dimensional regression Nadaraya-Watson, 196436:27
Distribution Regression Problem Definition38:02
The kernel-kernel estimator39:22
Assumptions42:56
Bounding the risk45:14
Doubling Measure47:54
Examples for finite doubling dimension48:44
Rates50:32
Proofs50:52
Why small ball probabilities?51:55
Proof continued…52:04
Distribution Regression52:17
Take Me Home!52:58