Energy-constrained discriminant analysis
Name
Philips-2009-Energy-constrained discriminant analysis.pdf
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138.33 KB
Format
Adobe PDF
Checksum (MD5)
09240b403430b3cbf82f5cd5e00004b1
Author(s)
Philips, Scott M.
Berisha, Visar
Spanias, Andreas
Date Issued
May 2009
Journal
IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. ICASSP 2009
Publisher
Institute of Electrical and Electronics Engineers
Citation
Philips, S., V. Berisha, and A. Spanias. “Energy-constrained discriminant analysis.” Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on. 2009. 3281-3284. © 2009 IEEE.
Version
Final published version
Abstract
Dimensionality reduction algorithms have become an indispensable tool for working with high-dimensional data in classification. Linear discriminant analysis (LDA) is a popular analysis technique used to project high-dimensional data into a lower-dimensional space while maximizing class separability. Although this technique is widely used in many applications, it suffers from overfitting when the number of training examples is on the same order as the dimension of the original data space. When overfitting occurs, the direction of the LDA solution can be dominated by low-energy noise and therefore the solution becomes non-robust to unseen data. In this paper, we propose a novel algorithm, energy-constrained discriminant analysis (ECDA), that overcomes the limitations of LDA by finding lower dimensional projections that maximize inter-class separability, while also preserving signal energy. Our results show that the proposed technique results in higher classification rates when compared to comparable methods. The results are given in terms of SAR image classification, however the algorithm is broadly applicable and can be generalized to any classification problem.
MIT Department
Lincoln Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICASSP.2009.4960325
