TY - GEN
T1 - A variant of the trace quotient formulation for dimensionality reduction
AU - Wang, Peng
AU - Shen, Chunhua
AU - Zheng, Hong
AU - Ren, Zhang
PY - 2010
Y1 - 2010
N2 - Due to its importance to classification and clustering, dimensionality reduction or distance metric learning has been studied in depth in recent years. In this work, we demonstrate the weakness of a widely-used class separability criterion - trace quotient for dimensionality reduction - and propose new criteria for the dimensionality reduction problem. The proposed optimization problem can be efficiently solved using semidefinite programming, similar to the technique in [1]. Experiments on classification and clustering are performed to evaluate the proposed algorithm. Results show the advantage of the our proposed algorithm.
AB - Due to its importance to classification and clustering, dimensionality reduction or distance metric learning has been studied in depth in recent years. In this work, we demonstrate the weakness of a widely-used class separability criterion - trace quotient for dimensionality reduction - and propose new criteria for the dimensionality reduction problem. The proposed optimization problem can be efficiently solved using semidefinite programming, similar to the technique in [1]. Experiments on classification and clustering are performed to evaluate the proposed algorithm. Results show the advantage of the our proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=78650460790&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-12297-2_27
DO - 10.1007/978-3-642-12297-2_27
M3 - Conference contribution
SN - 3642122965
SN - 9783642122965
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 277
EP - 286
BT - Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
T2 - 9th Asian Conference on Computer Vision, ACCV 2009
Y2 - 23 September 2009 through 27 September 2009
ER -