Background: The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem...
Peter A. DiMaggio Jr., Scott R. McAllister, Christ...
We extend the "Sparse LDA" algorithm of [7] with new sparsity bounds on 2-class separability and efficient partitioned matrix inverse techniques leading to 1000-fold spe...
In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respe...
Given a data matrix, the problem of finding dense/uniform sub-blocks in the matrix is becoming important in several applications. The problem is inherently combinatorial since th...
This paper presents a new algorithm for feature generation, which is approximately derived based on geometrical interpretation of the Fisher linear discriminant analysis. In a fiel...