Fast semi-supervised discriminative component analysis
Jaakko Peltonen, Jacob Goldberger and Samuel Kaski
In: MLSP 2007, 27-29 August 2007, Thessaloniki, Greece.
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.