Supervised Dimensionality Reduction with Principal Component Analysis
- Shipeng Yu | University of Munich, Germany
Principal component analysis (PCA) is widely applied for unsupervised dimensionality reduction. When labels of data are available, e.g., in a classification or regression task, PCA is however not able to use this information. In this talk I will present our recent work on supervised dimensionality reduction, where the outputs (i.e., supervised information) can be (multi-label) classification labels and regression values. The first approach is based on a supervised latent variable model, and it turns out to solve an generalized eigenvalue problem. The second approach goes beyond the first one and can handle the more interesting semi-supervised setting, i.e., only part of the data are labeled. In this approach learning is done via an efficient EM algorithm. Both approaches can be kernelized to handle non-linear mappings. They are compared with other competitors on various data sets and show very encouraging performance.
Speaker Details
Shipeng Yu is currently a Ph.D. candidate at University of Munich, Germany and is also a guest research scientist at Siemens Corporate Technology (the central research lab of Siemens) in Munich. Before coming to Germany he obtained his B.Sc. and M.Sc. at Peking University, China with major in mathematics. He was a visiting student at MSR Asia from 09/2001 to 08/2003, and was a research intern at MSR Cambridge from 07/2005 to 09/2005.
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Jeff Running
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