We propose a method based on sparse representation
(SR) to cluster data drawn from multiple low-dimensional
linear or affine subspaces embedded in a high-dimensional
space. Our ...
Abstract. A major challenge in pervasive computing is to learn activity patterns, such as bathing and cleaning from sensor data. Typical sensor deployments generate sparse datasets...
In this paper we explore the impact of the block shape on blocked and vectorized versions of the Sparse Matrix-Vector Multiplication (SpMV) kernel and build upon previous work by ...
Vasileios Karakasis, Georgios I. Goumas, Nectarios...
Abstract-General information about a class of objects, such as human faces or teeth, can help to solve the otherwise ill-posed problem of reconstructing a complete surface from spa...
Volker Blanz, Albert Mehl, Thomas Vetter, Hans-Pet...
We address the problem of computing joint sparse representation of visual signal across multiple kernel-based representations. Such a problem arises naturally in supervised visual...