In this paper we propose the Possibilistic C-Means in Feature Space and the One-Cluster Possibilistic C-Means in Feature Space algorithms which are kernel methods for clustering in...
Maurizio Filippone, Francesco Masulli, Stefano Rov...
— We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky...
Color space conversion is an important kernel in multimedia codecs such as JPEG and MPEG. When implemented using SIMD instructions, however, the performance improvement is often l...
Asadollah Shahbahrami, Ben H. H. Juurlink, Stamati...
We present a new streaming algorithm for maintaining an -kernel of a point set in Rd using O((1/(d-1)/2 ) log(1/)) space. The space used by our algorithm is optimal up to a small ...
In this paper, we extend the Hilbert space embedding approach to handle conditional distributions. We derive a kernel estimate for the conditional embedding, and show its connecti...
Le Song, Jonathan Huang, Alexander J. Smola, Kenji...