502 research outputs found
Quality-based Multimodal Classification Using Tree-Structured Sparsity
Recent studies have demonstrated advantages of information fusion based on
sparsity models for multimodal classification. Among several sparsity models,
tree-structured sparsity provides a flexible framework for extraction of
cross-correlated information from different sources and for enforcing group
sparsity at multiple granularities. However, the existing algorithm only solves
an approximated version of the cost functional and the resulting solution is
not necessarily sparse at group levels. This paper reformulates the
tree-structured sparse model for multimodal classification task. An accelerated
proximal algorithm is proposed to solve the optimization problem, which is an
efficient tool for feature-level fusion among either homogeneous or
heterogeneous sources of information. In addition, a (fuzzy-set-theoretic)
possibilistic scheme is proposed to weight the available modalities, based on
their respective reliability, in a joint optimization problem for finding the
sparsity codes. This approach provides a general framework for quality-based
fusion that offers added robustness to several sparsity-based multimodal
classification algorithms. To demonstrate their efficacy, the proposed methods
are evaluated on three different applications - multiview face recognition,
multimodal face recognition, and target classification.Comment: To Appear in 2014 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2014
Sparsity-Inducing Fuzzy Subspace Clustering
This paper considers a fuzzy subspace clustering problem and proposes to introduce an original sparsity-inducing regularization term. The minimization of this term, which involves a l penalty, is considered from a geometric point of view and a novel proximal operator is derived. A subspace clustering algorithm, Prosecco, is proposed to optimize the cost function using both proximal and alternate gradient descent. Experiments comparing this algorithm to the state of the art in sparse fuzzy subspace clustering show the relevance of the proposed approach
Various Approaches of Support vector Machines and combined Classifiers in Face Recognition
In this paper we present the various approaches used in face recognition from 2001-2012.because in last decade face recognition is using in many fields like Security sectors, identity authentication. Today we need correct and speedy performance in face recognition. This time the face recognition technology is in matured stage because research is conducting continuously in this field. Some extensions of Support vector machine (SVM) is reviewed that gives amazing performance in face recognition.Here we also review some papers of combined classifier approaches that is also a dynamic research area in a pattern recognition
Learning with Clustering Structure
We study supervised learning problems using clustering constraints to impose
structure on either features or samples, seeking to help both prediction and
interpretation. The problem of clustering features arises naturally in text
classification for instance, to reduce dimensionality by grouping words
together and identify synonyms. The sample clustering problem on the other
hand, applies to multiclass problems where we are allowed to make multiple
predictions and the performance of the best answer is recorded. We derive a
unified optimization formulation highlighting the common structure of these
problems and produce algorithms whose core iteration complexity amounts to a
k-means clustering step, which can be approximated efficiently. We extend these
results to combine sparsity and clustering constraints, and develop a new
projection algorithm on the set of clustered sparse vectors. We prove
convergence of our algorithms on random instances, based on a union of
subspaces interpretation of the clustering structure. Finally, we test the
robustness of our methods on artificial data sets as well as real data
extracted from movie reviews.Comment: Completely rewritten. New convergence proofs in the clustered and
sparse clustered case. New projection algorithm on sparse clustered vector
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