Dimensionality reduction techniques for proximity problems

Abstract--We propose an eigenvector-based heteroscedastic linear dimension reduction (LDR) technique for multiclass data. The technique is based on a heteroscedastic two-class technique which utilizes the so-called Chernoff criterion, and successfully ...

Exploratory data analysis often relies heavily on visual methods because of the power of the human eye to detect structures. For large, multidimensional data sets which cannot be easily visualized, the number of dimensions of the data can be reduced by ...

To improve effectively the performance on spoken emotion recognition, it is needed to perform nonlinear dimensionality reduction for speech data lying on a nonlinear manifold embedded in a high-dimensional acoustic space. In this paper, a new supervised ...