@inproceedings{6c9fbeb152744dbf9de5a751e63a3746,

title = "PSDBoost: Matrix-generation linear programming for positive semidefinite matrices learning",

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.",

author = "Chunhua Shen and Alan Welsh and Lei Wang",

year = "2009",

language = "English",

isbn = "9781605609492",

series = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",

publisher = "Neural Information Processing Systems",

pages = "1473--1480",

booktitle = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",

note = "22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 ; Conference date: 08-12-2008 Through 11-12-2008",

}