Online tracking of linear subspaces

被引：6
|
作者：
Crammer, Koby [1 ]
机构：
[1] Univ Penn, Dept Comp & Informat Sci, Philadelphia, PA 19104 USA
来源：
LEARNING THEORY, PROCEEDINGS | 2006年 / 4005卷
关键词：
D O I：
10.1007/11776420_33
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We address the problem of online de-noising a stream of input points. We assume that the clean data is embedded in a linear subspace. We present two online algorithms for tracking subspaces and, as a consequence, de-noising. We also describe two regularization schemas which improve the resistance to noise. We analyze the algorithms in the loss bound model, and specify some of their properties. Preliminary simulations illustrate the usefulness of our algorithms.
引用
收藏
页码：438 / 452
页数：15
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