Home Page

Papers

Submissions

News

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Login



RSS Feed

Online matrix prediction for sparse loss matrices

Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto, Koji Tsuda
Proceedings of the Sixth Asian Conference on Machine Learning, pp. 250–265, 2014

Abstract

We consider an online matrix prediction problem. The FTRL is a famous method to deal with online prediction task, which makes prediction by minimizing cumulative loss function and regularizer function. There are three popular regularizer functions for matrices, Frobenius norm, quantum relative entropy and log-determinant. We propose a FTRL based algorithm with log-determinant as regularizer and show regret bound of algorithm. Our main contribution is to show that log-determinant regularization is efficient when sparse loss function setting. We also show the optimal performance algorithm for online collaborative filtering problem with log-determinant regularization.

Related Material