Feature Reduction Based on Sum-of-SNR (SOSNR) Optimization
Paper i proceeding, 2014

Dimensionality reduction plays an important role in machine learning techniques. In classification, data transformation aims to reduce the number of feature dimensions, whereas attempts to enhance the class separability. To this end, we propose a new classifier-independent criterion called 'Sum-of-Signal-to-Noise-Ratio' (SoSNR). A framework designed for maximization with respect to this criterion is presented and three types of algorithms, respectively based on (1) gradient, (2) deflation and (3) sparsity, are proposed. The techniques are conducted on standard UCI databases and compared to other related methods. Results show trade-offs between computational complexity and classification accuracy among different approaches.

classification

Fisher's Score

Sum-of-SNR

SODA

feature reduction

Författare

Yinan Yu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Tomas McKelvey

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

S.Y. Kung

Princeton University

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

6756-6760 6854908

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Elektroteknik och elektronik

DOI

10.1109/ICASSP.2014.6854908

ISBN

978-147992892-7

Mer information

Skapat

2017-10-07