Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently, rese...
Adam Kowalczyk, Alex J. Smola, Peter A. Flach, Tho...
Abstract. In this paper, we propose a new method for image classification, in which matrix based kernel features are designed to capture the multiple similarities between images in...
In this paper, we address the pre-image problem in kernel principal component analysis (KPCA). The preimage problem finds a pattern as the pre-image of a feature vector defined in...
In recent years there has been a lot of interest in designing principled classification algorithms over multiple cues, based on the intuitive notion that using more features shou...
— We propose a feature selection criterion based on kernel discriminant analysis (KDA) for an -class problem, which finds eigenvectors on which the projected class data are loca...