---
title: "Greedy Learning of Generalized Low-Rank Models"
authors:
  - "Quanming Yao"
  - "James T. Kwok"
doi: ""
url: "https://ijcai.org/proceedings/2016/327"
pdf: "https://ijcai.org/proceedings/2016/0327.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2294-2299"
track: ""
keywords:
---

# Greedy Learning of Generalized Low-Rank Models

Quanming Yao, James T. Kwok

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 2294-2299.

## Abstract

Learning of low-rank matrices is fundamental to many machine learning applications. A state-of-the-art algorithm is the rank-one matrix pursuit (R1MP). However, it can only be used in matrix completion problems with the square loss. In this paper, we develop a more flexible greedy algorithm for generalized low-rank models whose optimization objective can be smooth or nonsmooth, general convex or strongly convex. The proposed algorithm has low per-iteration time complexity and fast convergence rate.Experimental results show that it is much faster than the state-of-the-art,with comparable or even better prediction performance.