L1 graph based on sparse coding for feature selection

Authors:
Jin Xu;Guang Yang;Hong Man;Haibo He
Affiliations:
Operations and Information Management Department, University of Pennsylvania, PA;Department of Electrical and Computer Engineering, Stevens Institute of Technology, NJ;Department of Electrical and Computer Engineering, Stevens Institute of Technology, NJ;Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, RI
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

Citing 8
Cited 0

Unsupervised Feature Selection Using Feature Similarity

IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection

The Journal of Machine Learning Research
Multiclass Object Recognition with Sparse, Localized Features

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Dictionary learning based on Laplacian score in sparse coding

MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
DCPE co-training for classification

Neurocomputing
Underdetermined blind source separation based on sparse representation

IEEE Transactions on Signal Processing
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

IEEE Transactions on Information Theory
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In machine learning and pattern recognition, feature selection has been a very active topic in the literature. Unsupervised feature selection is challenging due to the lack of label which would supply the categorical information. How to define an appropriate metric is the key for feature selection. In this paper, we propose a "filter" method for unsupervised feature selection, which is based on the geometry properties of ℓ1 graph. ℓ1 graph is constructed through sparse coding. The graph establishes the relations of feature subspaces and the quality of features is evaluated by features' local preserving ability. We compare our method with classic unsupervised feature selection methods (Laplacian score and Pearson correlation) and supervised method (Fisher score) on benchmark data sets. The classification results based on support vector machine, k-nearest neighbors and multi-layer feed-forward networks demonstrate the efficiency and effectiveness of our method.