TY - GEN
T1 - Collaborative representation, sparsity or nonlinearity
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
AU - Chen, Xu
AU - Ramadge, Peter Jeffrey
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Recent studies have suggested that the critical aspect of sparse representation-based classification (SRC) is collaborative representation, rather than sparsity. This has given rise to fast collaborative representation-based classification using 2-norm regularized least squares (CRC-RLS). This paper digs deeper into the difference between SRC and CRC-RLS. We show that linear coding schemes such as CRC-RLS share a common pairwise boundary class B. Moreover, the corresponding pairwise classifiers can be realized by quadratic SVMs. Using three datasets, we show empirically that collaborative representations are not always required, and that a quadratic SVM has superior generalization over CRC-RLS, with fast classification times. However, SRC exhibits the best prediction accuracy. This leads us to posit that the nonlinear coding of SRC is a key attribute.
AB - Recent studies have suggested that the critical aspect of sparse representation-based classification (SRC) is collaborative representation, rather than sparsity. This has given rise to fast collaborative representation-based classification using 2-norm regularized least squares (CRC-RLS). This paper digs deeper into the difference between SRC and CRC-RLS. We show that linear coding schemes such as CRC-RLS share a common pairwise boundary class B. Moreover, the corresponding pairwise classifiers can be realized by quadratic SVMs. Using three datasets, we show empirically that collaborative representations are not always required, and that a quadratic SVM has superior generalization over CRC-RLS, with fast classification times. However, SRC exhibits the best prediction accuracy. This leads us to posit that the nonlinear coding of SRC is a key attribute.
KW - Collaborative Representation
KW - Machine Learning
KW - Sparse Representation
UR - http://www.scopus.com/inward/record.url?scp=84905234335&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905234335&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6854600
DO - 10.1109/ICASSP.2014.6854600
M3 - Conference contribution
AN - SCOPUS:84905234335
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5227
EP - 5231
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 May 2014 through 9 May 2014
ER -