Constructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification. This paper proposes a novel no...
Liansheng Zhuang, Haoyuan Gao, Zhouchen Lin, Yi Ma...
We consider the problem of modeling network interactions and identifying latent groups of network nodes. This problem is challenging due to the facts i) that the network nodes are...
We present a graph partitioning algorithm that aims at partitioning a sparse matrix into a block-diagonal form, such that any two consecutive blocks overlap. We denote this form o...
Guy Antoine Atenekeng Kahou, Laura Grigori, Masha ...
Sparse matrices are first class objects in many VHLLs (very high level languages) used for scientific computing. They are a basic building block for various numerical and combinat...
Large sparse matrices play important role in many modern information retrieval methods. These methods, such as clustering, latent semantic indexing, performs huge number of computa...
Collaborative Filtering (CF) aims at finding patterns in a sparse matrix of contingency. It can be used for example to mine the ratings given by users on a set of items. In this p...
Creating a high throughput sparse matrix vector multiplication (SpMxV) implementation depends on a balanced system design. In this paper, we introduce the innovative SpMxV Solver ...
Junqing Sun, Gregory D. Peterson, Olaf O. Storaasl...
In this paper, we consider alternate ways of storing a sparse matrix and their effect on computational speed. They involve keeping both the indices and the non-zero elements in t...
Standard restructuring compiler tools are based on polyhedral algebra and cannot be used to analyze or restructure sparse matrix codes. We have recently shown that tools based on ...
Abstract. We present a relational algebra based framework for compiling e cient sparse matrix code from dense DO-ANY loops and a speci cation of the representation of the sparse ma...