Fast Semi-Supervised Discriminative Component Analysis

Reference:

Jaakko Peltonen, Jacob Goldberger, and Samuel Kaski. Fast semi-supervised discriminative component analysis. In Konstantinos Diamantaras, Tülay Adali, Ioannis Pitas, Jan Larsen, Theophilos Papadimitriou, and Scott Douglas, editors, Machine Learning for Signal Processing XVII, pages 312–317. IEEE, 2007. Preprint pdf at http://www.cis.hut.fi/projects/mi/papers/mlsp07.pdf.

Abstract:

We introduce a method that learns a class-discriminative subspace or discriminative components of data. Such a subspace is useful for visualization, dimensionality reduction, feature extraction, and for learning a regularized distance metric. We learn the subspace by optimizing a probabilistic semiparametric model, a mixture of Gaussians, of classes in the subspace. The semiparametric modeling leads to fast computation (O(N) for N samples) in each iteration of optimization, in contrast to recent nonparametric methods that take O(N^2) time, but with equal accuracy. Moreover, we learn the subspace in a semi-supervised manner from three kinds of data: labeled and unlabeled samples, and unlabeled samples with pairwise constraints, with a unified objective.

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Suggested BibTeX entry:

@inproceedings{Peltonen07mlsp,
    author = {Jaakko Peltonen and Jacob Goldberger and Samuel Kaski},
    booktitle = {Machine Learning for Signal Processing XVII},
    editor = {Konstantinos Diamantaras and T{\"u}lay Adali and Ioannis Pitas and Jan Larsen and Theophilos Papadimitriou and Scott Douglas},
    note = {Preprint pdf at \url{http://www.cis.hut.fi/projects/mi/papers/mlsp07.pdf}},
    pages = {312-317},
    publisher = {IEEE},
    title = {Fast Semi-Supervised Discriminative Component Analysis},
    year = {2007},
}