@inproceedings{ouyed_recognizing_2018,

title = {Recognizing human interactions using group feature relevance in multinomial kernel logistic regression},

author = {O. Ouyed and M. S. Allili},

editor = {Rus V. Brawner K.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067977954&partnerID=40&md5=1c8d720de570fc565bca3741c107bc9a},

isbn = {978-1-57735-796-4},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018},

pages = {541–545},

publisher = {AAAI press},

abstract = {We propose a supervised approach incorporating group feature sparsity in multi-class kernel logistic regression (GFR-MKLR). The need for group sparsity arises in several practical situations where a subset of a set of factors can explain a predicted variable and each factor consists of a group of variables. We apply our approach for predicting human interactions based on body parts motion (e.g., hands, legs, head, etc.) where image features are organised in groups corresponding to body parts. Our approach, leads to sparse models by assigning weights to groups of features having the highest discrimination between different types of interactions. Experiments conducted on the UT-Interaction dataset have demonstrated the performance of our method with regard to stat-of-art methods. Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.},

keywords = {Art methods, Artificial intelligence, Feature relevance, Group sparsities, Human interactions, Image features, Kernel logistic regression, Multinomial kernels, regression analysis, Sparse models},

pubstate = {published},

tppubtype = {inproceedings}

}