Fifth International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2007)
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
Full Paper (reprinted with permission from Firenze University Press)
Bibliographic reference. Goddard, J. C. / Martínez, F. M. / Schlotthauer, G. / Torres, M. E. / Rufiner, H. L. (2007): "Visualization of normal and pathological speech data", In MAVEBA-2007, 33-36.