Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/80495
Type: | Conference paper |
Title: | PSDBoost: matrix-generation linear programming for positive semidefinite matrices learning |
Author: | Shen, C. Welsh, A. Wang, L. |
Citation: | Advances in Neural Information Processing Systems 21: proceedings of the 2008 Conference / D. Koller, Y. Bengio, D. Schuurmans, L. Bottou, and A. Culotta (eds.): pp.1473-1480 |
Publisher: | Curran Associates, Inc |
Issue Date: | 2008 |
ISBN: | 9781605609492 |
Conference Name: | Annual Conference on Neural Information Processing Systems (22nd : 2008 : Vancouver, Canada) |
Statement of Responsibility: | Chunhua Shen, Alan Welsh, Lei Wang |
Abstract: | In this work, we consider the problem of learning a positive semidefinite matrix. The critical issue is how to preserve positive semidefiniteness during the course of learning. Our algorithm is mainly inspired by LPBoost [1] and the general greedy convex optimization framework of Zhang [2]. We demonstrate the essence of the algorithm, termed PSDBoost (positive semidefinite Boosting), by focusing on a few different applications in machine learning. The proposed PSDBoost algorithm extends traditional Boosting algorithms in that its parameter is a positive semidefinite matrix with trace being one instead of a classifier. PSDBoost is based on the observation that any trace-one positive semidefinite matrix can be decomposed into linear convex combinations of trace-one rank-one matrices, which serve as base learners of PSDBoost. Numerical experiments are presented. |
Rights: | © 2009 NIPS Foundation | All Rights Reserved. |
Published version: | http://papers.nips.cc/paper/3611-psdboost-matrix-generation-linear-programming-for-positive-semidefinite-matrices-learning |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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