Kernelized sorting is an approach for matching objects from two sources (or domains) that does not require any prior notion of similarity between objects across the two sources. U...
Jagadeesh Jagarlamudi, Seth Juarez, Hal Daum&eacut...
We claim and present arguments to the effect that a large class of manifold learning algorithms that are essentially local and can be framed as kernel learning algorithms will suf...
To recognize people by their gait from a sequence of images, we have proposed a statistical approach which combined eigenspace transformation (EST) with canonical space transforma...
This paper describes how to reduce the burden of parallel programming by utilizing relevant parallel programs. Parallel algorithms are divided into four classes and a case base fo...
Abstract--Reconstruction algorithms for fluorescence tomography have to address two crucial issues : (i) the ill-posedness of the reconstruction problem, (ii) the large scale of nu...