Discriminative Sparse Neighbor Approximation for Imbalanced Learning

Discriminative Sparse Neighbor Approximation for Imbalanced Learning

Data imbalance is common in many vision tasks where one or more classes are rare. Without addressing this issue, conventional methods tend to be biased toward the majority class with poor predictive accuracy for the minority class. These methods further deteriorate on small, imbalanced data that have a large degree of class overlap. In this paper, we propose a novel discriminative sparse neighb...

Learning Sparse Dictionaries for Sparse Signal Approximation

An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The structure is based on a sparsity model of the dictionary atoms over a base dictionary. The sparse dictionary provides efficient forward and adjoint operators, has a compact representation, and can be effectively trained from given example data. In this, the sparse structure bridges the...

Learning Sparse Mixture Models for Discriminative Classification

Recently Saul and Lee proposed a mixture model for discriminative classification of non-negative data via non-negative matrix factorization for feature extraction. In order to improve the generalization, this paper considers a sparse version of the model. The basic idea is to minimize the sum of the weights of un-normalized mixture models for posterior distributions according to regularization ...

Discriminative Learning of the Prototype Set for Nearest Neighbor Classification

The nearest neighbor rule is one of the most widely used models for classification and selecting a compact set of prototype instances is an important problem for its applications. Many existing approaches on the prototype selection problem rely on instance-based analyses and local criteria on the class distribution, which are intractable for numerical optimization techniques. In this paper, we ...

Sparse Greedy Matrix Approximation for Machine Learning