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A Transductive Framework of Distance Metric Learning by Spectral Dimensionality Reduction
Published on Jun 23, 20075995 Views
Distance metric learning and nonlinear dimensionality reduction are two interesting and active topics in recent years. However, the connection between them is not thoroughly studied yet. In this paper
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A Transductive Framework of Distance Metric Learning by Spectral Dimensionality Reduction00:00
Metric Learning: What does it do?00:23
What’s good?01:07
Endless Learning Cycle01:53
How to learn?02:26
Wait a minute…03:14
Dimensionality Reduction03:51
And Metric Learning?04:48
A Metric Learning Formulation05:22
Graph Transduction06:45
The Euclidean Assumption07:56
And Kernels09:10
Learning a Kernel09:46
Dimensionality Reduction11:02
More to give: RKHS regularization11:58
Moving y to the weights13:07
The parameter λ14:23
Experiments: Two Moons15:21
Experiments: UCI Data16:11
Experiments: MNIST16:35
Conclusion17:07
Ongoing Work17:42
Beyond Euclidean18:11
Thanks!19:33