TY - GEN
T1 - Feature shift detection
AU - Glazer, Assaf
AU - Lindenbaum, Michael
AU - Markovitch, Shaul
PY - 2012/12/1
Y1 - 2012/12/1
N2 - During training and classification, instances are drawn from the instance space and mapped to the feature space. We focus on the problem of detecting hidden changes in the functions that map instances to feature vectors during classification. We call such changes feature shift and introduce an on-line method for detecting it. Our method is based on a robust similarity measure that uses one-class SVM to monitor distributional changes in the feature space. Unlike previous methods, ours can distinguish between changes in priors and feature shift. The method is empirically evaluated on visual categorization tasks and its advantage verified.
AB - During training and classification, instances are drawn from the instance space and mapped to the feature space. We focus on the problem of detecting hidden changes in the functions that map instances to feature vectors during classification. We call such changes feature shift and introduce an on-line method for detecting it. Our method is based on a robust similarity measure that uses one-class SVM to monitor distributional changes in the feature space. Unlike previous methods, ours can distinguish between changes in priors and feature shift. The method is empirically evaluated on visual categorization tasks and its advantage verified.
UR - http://www.scopus.com/inward/record.url?scp=84874556689&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84874556689
SN - 9784990644109
T3 - Proceedings - International Conference on Pattern Recognition
SP - 1383
EP - 1386
BT - ICPR 2012 - 21st International Conference on Pattern Recognition
T2 - 21st International Conference on Pattern Recognition, ICPR 2012
Y2 - 11 November 2012 through 15 November 2012
ER -