Dirichlet Mixture Matching Projection for supervised linear dimensionality reduction of proportional data

Walid Masoudimansour; Nizar Bouguila

ICASSP 2017accepted0 citations

Dirichlet Mixture Matching Projection for supervised linear dimensionality reduction of proportional data

Abstract

An effective novel algorithm to reduce the dimensionality of labeled proportional data is presented which uses an optimal linear projection to project the data into a low-dimensional space. Assuming that each class of the projected data is generated by a mixture of Dirichlet distributions, KL-divergence is used as a dissimilarity measure to maximize the mutual information of projected classes, thus improving separability. Finally, genetic algorithm is used to find such optimal projection. The proposed algorithm is designed as a preprocessing step for binary classification of proportional data, however, it can project multimodal data as well due to use of mixtures and, therefore, can be used for multiclass classification. Experiments show that the proposed technique is effective, and constantly produces better results compared to well-known algorithms from the same category.

BibTeX

@inproceedings{icassp2017_dirichletmixture,
  title = {Dirichlet Mixture Matching Projection for supervised linear dimensionality reduction of proportional data},
  author = {Walid Masoudimansour and Nizar Bouguila},
  booktitle = {ICASSP 2017},
  year = {2017}
}