TY - GEN
T1 - Optimizing feature selection using laplace similarity in occluded human motion recognition
AU - Khoury, Mehdi
AU - Kubota, Naoyuki
AU - Liu, Honghai
PY - 2010
Y1 - 2010
N2 - In this paper, feature selection is used to allow the identification of critical attributes before the reconstruction of occluded data in 3d human motion classification. This work presents Fuzzy Laplace Similarity: a new fuzzy similarity relation used in the context of fuzzy rough feature selection. The measure of similarity is critical to the performance of the selection phase, which in turn will determine the performance of a classifier. Results over various datasets show that Fuzzy Laplace Similarity improves consistently the quality of the fuzzy rough feature selection process and performs well compared to other fuzzy similarity relations.
AB - In this paper, feature selection is used to allow the identification of critical attributes before the reconstruction of occluded data in 3d human motion classification. This work presents Fuzzy Laplace Similarity: a new fuzzy similarity relation used in the context of fuzzy rough feature selection. The measure of similarity is critical to the performance of the selection phase, which in turn will determine the performance of a classifier. Results over various datasets show that Fuzzy Laplace Similarity improves consistently the quality of the fuzzy rough feature selection process and performs well compared to other fuzzy similarity relations.
KW - Fuzzy laplace similarity
KW - Fuzzy rough feature selection
KW - Human motion analysis
UR - https://www.scopus.com/pages/publications/78651418888
M3 - 会议稿件
AN - SCOPUS:78651418888
SN - 9781424496730
T3 - 2010 World Automation Congress, WAC 2010
BT - 2010 World Automation Congress, WAC 2010
T2 - 2010 World Automation Congress, WAC 2010
Y2 - 19 September 2010 through 23 September 2010
ER -