@article{ouyed_feature_2018,

title = {Feature weighting for multinomial kernel logistic regression and application to action recognition},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035104467&doi=10.1016%2fj.neucom.2017.10.024&partnerID=40&md5=09687b392a405be4338799a750932cf3},

doi = {10.1016/j.neucom.2017.10.024},

issn = {09252312},

year  = {2018},

date = {2018-01-01},

journal = {Neurocomputing},

volume = {275},

pages = {1752–1768},

abstract = {Multinominal kernel logistic regression (MKLR) is a supervised classification method designed for separating classes with non-linear boundaries. However, it relies on the assumption that all features are equally important, which may decrease classification performance when dealing with high-dimensional and noisy data. We propose an approach for embedding feature relevance in multinomial kernel logistic regression. Our approach, coined fr-MKLR, generalizes MKLR by introducing a feature weighting scheme in the Gaussian kernel and using the so-called ℓ0-“norm” as sparsity-promoting regularization. Therefore, the contribution of each feature is tuned according to its relevance for classification which leads to more generalizable and interpretable sparse models for classification. Application of our approach to several standard datasets and video action recognition has provided very promising results compared to other methods. © 2017 Elsevier B.V.},

note = {Publisher: Elsevier B.V.},

keywords = {Action recognition, article, Classification, classification algorithm, Classification performance, Computer applications, controlled study, embedding, Feature relevance, feature relevance for multinomial kernel logistic regression, Feature weighting, Kernel logistic regression, kernel method, Learning, mathematical computing, Multinomial kernels, multinominal kernel logistic regression, Neural networks, priority journal, recognition, regression analysis, simulation, sparse modeling, Sparse models, sparse multinomial logistic regression, sparsity promoting regularization, standard, Supervised classification},

pubstate = {published},

tppubtype = {article}

}

@article{renaud_sexual_2010,

title = {Sexual affordances, perceptual-motor invariance extraction and intentional nonlinear dynamics: Sexually deviant and non-deviant patterns in male subjects},

author = {P. Renaud and M. Goyette and S. Chartier and S. Zhornitski and D. Trottier and J. -L. Rouleau and J. Proulx and P. Fedoroff and J. -P. Bradford and B. Dassylva and S. Bouchard},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78049436590&partnerID=40&md5=f7c928ae6a9624c1c3704748a20f03ec},

issn = {10900578},

year  = {2010},

date = {2010-01-01},

journal = {Nonlinear Dynamics, Psychology, and Life Sciences},

volume = {14},

number = {4},

pages = {463–489},

abstract = {Sexual arousal and gaze behavior dynamics are used to characterize deviant sexual interests in male subjects. Pedophile patients and non-deviant subjects are immersed with virtual characters depicting relevant sexual features. Gaze behavior dynamics as indexed from correlation dimensions (D2) appears to be fractal in nature and significantly different from colored noise (surrogate data tests and recurrence plot analyses were performed). This perceptual-motor fractal dynamics parallels sexual arousal and differs from pedophiles to non-deviant subjects when critical sexual information is processed. Results are interpreted in terms of sexual affordance, perceptual invariance extraction and intentional nonlinear dynamics. © 2010 Society for Chaos Theory in Psychology & Life Sciences.},

keywords = {adult, Arousal, article, behavior, computer interface, Computer Simulation, Computer-Assisted, Erotica, eye movement, Eye movements, human, Humans, Intention, male, mathematical computing, Middle Aged, Nonlinear Dynamics, nonlinear system, pathophysiology, Pedophilia, Penis, physiology, Plethysmography, psychological aspect, Psychomotor Performance, publication, reference value, Reference Values, Sexual Behavior, Signal processing, User-Computer Interface, vascularization},

pubstate = {published},

tppubtype = {article}

}

@article{renaud_presence_2007,

title = {Presence as determined by fractal perceptual-motor dynamics},

author = {P. Renaud and S. Chartier and G. Albert and J. Décarie and L. -G. Cournoyer and S. Bouchard},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33847713017&doi=10.1089%2fcpb.2006.9983&partnerID=40&md5=c1c6df654279a13b1553e9cfbf43acd0},

doi = {10.1089/cpb.2006.9983},

issn = {10949313},

year  = {2007},

date = {2007-01-01},

journal = {Cyberpsychology and Behavior},

volume = {10},

number = {1},

pages = {122–130},

abstract = {This paper presents a tentative model of the role of perceptual-motor dynamics in the emergence of the feeling of presence. A new method allowing the measure of how gaze probes three-dimensional space in immersion is used to support this model. Fractal computations of gaze behavior are shown to be more effective titan standard computations of eye movements in predicting presence. © Mary Ann Liebert, Inc.},

keywords = {adult, article, eye movement, Eye movements, female, gaze, human, Humans, immersion, male, mathematical computing, motor performance, perceptual motor dynamics, Psychomotor Performance, simulation, Social Environment, Social Perception, standard, three dimensional imaging, User-Computer Interface, virtual reality modeling language, Visual Perception},

pubstate = {published},

tppubtype = {article}

}