TY - GEN
T1 - Energy-constrained discriminant analysis
AU - Philips, Scott
AU - Berisha, Visar
AU - Spanias, Andreas
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Dimensionality reduction
KW - Discriminant analysis
KW - Machine learning
KW - Pattern recognition
KW - Principal components analysis
UR - http://www.scopus.com/inward/record.url?scp=70349213295&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70349213295&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2009.4960325
DO - 10.1109/ICASSP.2009.4960325
M3 - Conference contribution
AN - SCOPUS:70349213295
SN - 9781424423545
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3281
EP - 3284
BT - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
T2 - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Y2 - 19 April 2009 through 24 April 2009
ER -