It was prescriptive that an image matrix was transformed into a vector before the kernel-based subspace learning. In this paper, we take the Kernel Discriminant Analysis (KDA) alg...
Shuicheng Yan, Dong Xu, Lei Zhang, Benyu Zhang, Ho...
Kernel methods have gained a great deal of popularity in the machine learning community as a method to learn indirectly in highdimensional feature spaces. Those interested in rela...
Many applications in text and speech processing require the analysis of distributions of variable-length sequences. We recently introduced a general kernel framework, rational ker...
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...
Structural alignments are the most widely used tools for comparing proteins with low sequence similarity. The main contribution of this paper is to derive various kernels on prote...