37,866 research outputs found
Discriminative variable selection for clustering with the sparse Fisher-EM algorithm
The interest in variable selection for clustering has increased recently due
to the growing need in clustering high-dimensional data. Variable selection
allows in particular to ease both the clustering and the interpretation of the
results. Existing approaches have demonstrated the efficiency of variable
selection for clustering but turn out to be either very time consuming or not
sparse enough in high-dimensional spaces. This work proposes to perform a
selection of the discriminative variables by introducing sparsity in the
loading matrix of the Fisher-EM algorithm. This clustering method has been
recently proposed for the simultaneous visualization and clustering of
high-dimensional data. It is based on a latent mixture model which fits the
data into a low-dimensional discriminative subspace. Three different approaches
are proposed in this work to introduce sparsity in the orientation matrix of
the discriminative subspace through -type penalizations. Experimental
comparisons with existing approaches on simulated and real-world data sets
demonstrate the interest of the proposed methodology. An application to the
segmentation of hyperspectral images of the planet Mars is also presented
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning
representations and simultaneously discovering cluster structure in unlabeled
data by optimizing a discriminative loss function. As opposed to supervised
deep learning, this line of research is in its infancy, and how to design and
optimize suitable loss functions to train deep neural networks for clustering
is still an open question. Our contribution to this emerging field is a new
deep clustering network that leverages the discriminative power of
information-theoretic divergence measures, which have been shown to be
effective in traditional clustering. We propose a novel loss function that
incorporates geometric regularization constraints, thus avoiding degenerate
structures of the resulting clustering partition. Experiments on synthetic
benchmarks and real datasets show that the proposed network achieves
competitive performance with respect to other state-of-the-art methods, scales
well to large datasets, and does not require pre-training steps
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