Home Page

Papers

Submissions

News

Editorial Board

Special Issues

Open Source Software

Proceedings (PMLR)

Data (DMLR)

Transactions (TMLR)

Search

Statistics

Login

Frequently Asked Questions

Contact Us



RSS Feed

Subspace Learning with Partial Information

Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-Shwartz; 17(52):1−21, 2016.

Abstract

The goal of subspace learning is to find a $k$-dimensional subspace of $\mathbb{R}^d$, such that the expected squared distance between instance vectors and the subspace is as small as possible. In this paper we study subspace learning in a partial information setting, in which the learner can only observe $r \le d$ attributes from each instance vector. We propose several efficient algorithms for this task, and analyze their sample complexity.

[abs][pdf][bib]       
© JMLR 2016. (edit, beta)

Mastodon