TY - GEN
T1 - Extraction of attributes for visual object recognition and DNA microarray analysis
AU - Gadat, Sébastien
PY - 2005/1/1
Y1 - 2005/1/1
N2 - We introduce a new model formalizing selection of features from a large dictionary of variables that can be computed from a signal or an image. Features are extracted according to an efficiency criterion, on the basis of specified classification tasks. We estimate a probability distribution ℙ on the complete dictionary, which distributes its mass over the more efficient or informative components. The method is then tested on several problems of signal processing like face detection, handwritten digit recognition and DNA microarray analysis.
AB - We introduce a new model formalizing selection of features from a large dictionary of variables that can be computed from a signal or an image. Features are extracted according to an efficiency criterion, on the basis of specified classification tasks. We estimate a probability distribution ℙ on the complete dictionary, which distributes its mass over the more efficient or informative components. The method is then tested on several problems of signal processing like face detection, handwritten digit recognition and DNA microarray analysis.
U2 - 10.1109/ssp.2005.1628809
DO - 10.1109/ssp.2005.1628809
M3 - Conference contribution
AN - SCOPUS:33947169253
SN - 0780394046
SN - 9780780394049
T3 - IEEE Workshop on Statistical Signal Processing Proceedings
SP - 1370
EP - 1375
BT - 2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts
PB - IEEE Computer Society
T2 - 2005 IEEE/SP 13th Workshop on Statistical Signal Processing
Y2 - 17 July 2005 through 20 July 2005
ER -