DocumentCode :
2027398
Title :
Subspace techniques for large-scale feature selection
Author :
Heck, Larry P. ; McClellan, James H.
Author_Institution :
SRI International, Menlo Park, CA, USA
Volume :
4
fYear :
1993
fDate :
27-30 April 1993
Firstpage :
17
Abstract :
A novel feature selection algorithm is presented which outperforms the well-known SFS (sequential forward selection) and SBS (sequential backward selection) algorithms for large-scale problems. The approach utilizes the solution to the similar problem of large-scale feature extraction by choosing a subset of the original measurements that are closest to the space spanned by the extracted (transformed) features. The authors develop a computationally efficient Frobenius subspace distance metric for the subspace comparisons, which reduces the complexity from order N taken k at a time to order N/sup 3/ operations. Finally, sufficient conditions for optimality of the algorithm are presented that demonstrate the relationship between the feature extraction and the feature selection solutions.<>
Keywords :
computational complexity; feature extraction; large-scale systems; Frobenius subspace distance metric; algorithm; complexity; large-scale feature selection; pattern recognition; sufficient conditions for optimality;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on
Conference_Location :
Minneapolis, MN, USA
ISSN :
1520-6149
Print_ISBN :
0-7803-7402-9
Type :
conf
DOI :
10.1109/ICASSP.1993.319583
Filename :
319583
Link To Document :
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