Kernel Principal Component analysis through time for voice disorder classification.
Kernel Principal Component analysis is a nonlinear generalization of the popular linear multivariate analysis method. However, this method assumes that the observed data is independent, a disadvantage for many practical applications. In order to overcome this difficulty, the authors propose a combination of Kernel Principal Component analysis and hidden Markov models. The novelty of the proposed method consists mainly in the way in which a static dimensionality reduction technique has been combined with a classic mixture model in time, to enhance the capabilities of transformation, reduction and classification of voice disorder data. Experimental results show improvements in classification accuracies even with highly reduced representations of the two databases used.
Alvarez
Mauricio
M
Program of Electrical Engineering, Universidad Tecnológica de Pereira, Columbia. malvarez@utp.edu.co
Henao
Ricardo
R
Castellanos
Germán
G
Godino
Juan I
JI
Orozco
Alvaro
A
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D016247:Information Storage and Retrieval; D015233:Models, Statistical; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012984:Software; D013995:Time; D013997:Time Factors; D014831:Voice; D014832:Voice Disorders / Q000175:diagnosis* / Q000473:pathology