Techniques for the visualization of highdimensional data are common in exploratory data analysis and can be very useful for gaining an intuition into the structure of a data set. The classical method of principal component analysis is the one most often employed, however in recent years a number of other nonlinear techniques have been introduced. In the present paper, principal component analysis, and two newer methods, are applied to a set of speech data and their results are compared.
Index Terms. PCA, LLE, Kernel PCA
Cite as: Goddard, J.C., Martínez, F.M., Schlotthauer, G., Torres, M.E., Rufiner, H.L. (2007) Visualization of normal and pathological speech data. Proc. Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2007), 33-36
@inproceedings{goddard07_maveba,
author={J. C. Goddard and F. M. Martínez and G. Schlotthauer and M. E. Torres and H. L. Rufiner},
title={{Visualization of normal and pathological speech data}},
year=2007,
booktitle={Proc. Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2007)},
pages={33--36}
}