Discriminative components of data

Reference:

Jaakko Peltonen and Samuel Kaski. Discriminative components of data. IEEE Transactions on Neural Networks, 16:68–83, 2005. Preprint pdf at http://www.cis.hut.fi/projects/mi/papers/tnn04_preprint.pdf.

Abstract:

(Preprint abstract:) A simple probabilistic model is introduced to generalize classical linear discriminant analysis in finding components that are informative of or relevant for data classes. The components maximize the predictability of the class distribution which is asymptotically equivalent to (i) maximizing mutual information with the classes, and (ii) finding principal components in the so-called learning or Fisher metrics. The Fisher metric measures only distances that are relevant to the classes, that is, distances that cause changes in the class distribution. The components have applications in data exploration, visualization, and dimensionality reduction. In empirical experiments the method outperformed, in addition to more classical methods, a Renyi entropy-based alternative while having essentially equivalent computational cost.

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Suggested BibTeX entry:

@article{Peltonen05trnn,
    author = {Jaakko Peltonen and Samuel Kaski},
    journal = {IEEE Transactions on Neural Networks},
    note = {Preprint pdf at \url{http://www.cis.hut.fi/projects/mi/papers/tnn04_preprint.pdf}},
    pages = {68-83},
    title = {Discriminative components of data},
    volume = {16},
    year = {2005},
}