TY - GEN
T1 - Kernel Principal component analysis through time for voice disorder classification
AU - Alvarez, Mauricio
AU - Henao, Ricardo
AU - Castellanos, Germán
AU - Godino, Juan I.
AU - Orozco, Alvaro
N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15
PY - 2006/12/1
Y1 - 2006/12/1
N2 - Kernel Principal Component analysis is a non-linear 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. © 2006 IEEE.
AB - Kernel Principal Component analysis is a non-linear 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. © 2006 IEEE.
UR - http://ieeexplore.ieee.org/document/4463053/
UR - http://www.scopus.com/inward/record.url?scp=34047115285&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2006.260357
DO - 10.1109/IEMBS.2006.260357
M3 - Conference contribution
SN - 1424400325
SP - 5511
EP - 5514
BT - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ER -