---
title: "Joint Feature Selection and Subspace Learning"
authors:
  - "Quanquan Gu"
  - "Zhenhui Li"
  - "Jiawei Han"
doi: "10.5591/978-1-57735-516-8/IJCAI11-219"
url: "https://ijcai.org/proceedings/2011/219"
pdf: "https://ijcai.org/proceedings/2011/0219.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1294-1298"
track: ""
keywords:
---

# Joint Feature Selection and Subspace Learning

Quanquan Gu, Zhenhui Li, Jiawei Han

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1294-1298. DOI: [10.5591/978-1-57735-516-8/IJCAI11-219](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-219)

## Abstract

Dimensionality reduction is a very important topic in machine learning. It can be generally classified into two categories: feature selection and subspace learning. In the past decades, many methods have been proposed for dimensionality reduction. However, most of these works study feature selection and subspace learning independently. In this paper, we present a framework for joint feature selection and subspace learning. We reformulate the subspace learning problem and use L2,1-norm on the projection matrix to achieve row-sparsity, which leads to selecting relevant features and learning transformation simultaneously. We discuss two situations of the proposed framework, and present their optimization algorithms. Experiments on benchmark face recognition data sets illustrate that the proposed framework outperforms the state of the art methods overwhelmingly.