Sparse Fisher's linear discriminant analysis

Hasib Siddiqui; Hau Hwang

doi:10.1117/12.887693

7 February 2011 Sparse Fisher's linear discriminant analysis

Hasib Siddiqui, Hau Hwang

Proceedings Volume 7873, Computational Imaging IX; 78730C (2011) https://doi.org/10.1117/12.887693
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

Fisher's linear discriminant analysis (LDA) is traditionally used in statistics and pattern recognition to linearlyproject high-dimensional observations from two or more classes onto a low-dimensional feature space before classification. The computational complexity of the linear feature extraction method increases linearly with dimensionality of the observation samples. For high-dimensional signals, high computational cost can render the method unsuitable for implementation in real time. In this paper, we propose sparse Fisher's linear discriminant analysis, which allows one to search for lowdimensional subspaces, spanned by sparse discriminant vectors, in the high-dimensional space of observation samples from two classes. The sparsity constraints on the space of potential discriminant feature vectors are enforced using the sparse matrix transform (SMT) framework, proposed recently for regularized covariance estimation. Classical Fisher's LDA is a special case of sparse Fisher's LDA when the sparsity constraints on the feature vectors in the estimation algorithm are fully relaxed. The number of non-zero components in a discriminant direction estimated using our proposed discriminant analysis technique is tunable; this feature can be used to control the compromise between computational complexity and accuracy of the eventual classification algorithm. The experimental results discussed in the manuscript demonstrate the effectiveness of the new method for low-complexity data-classification applications.

Citation Download Citation

Hasib Siddiqui and Hau Hwang "Sparse Fisher's linear discriminant analysis", Proc. SPIE 7873, Computational Imaging IX, 78730C (7 February 2011); https://doi.org/10.1117/12.887693

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
14 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Antimony

Matrices

Statistical analysis

Radon

Control systems

Erbium

Facial recognition systems

Show All Keywords

Keywords/Phrases

Search In:

Publication Years