Efficient MATLAB Computations with Sparse and Factored Tensors

Efficient MATLAB Computations with Sparse and Factored Tensors

In this paper, the term tensor refers simply to a multidimensional or N -way array, and we consider how specially structured tensors allow for efficient storage and computation. First, we study sparse tensors, which have the property that the vast majority of the elements are zero. We propose storing sparse tensors using coordinate format and describe the computational efficiency of this scheme...

A MATLAB-Based Code Generator for Sparse Matrix Computations

We present a matrix language compiler CMC which translates annotated MATLAB scripts into Fortran 90 programs. Distinguishing features of CMC include its applicability to programs with sparse matrix computations and its capability of source-level optimization in MATLAB language. Different from other existing similar translators, CMC has an ability to generate codes based on information on the sh...

Sparse Computations with PEI

Factored sparse inverse covariance matrices

Most HMM-based speech recognition systems use Gaussian mixtures as observation probability density functions. An important goal in all such systems is to improve parsimony. One method is to adjust the type of covariance matrices used. In this work, factored sparse inverse covariance matrices are introduced. Based on U DU factorization, the inverse covariance matrix can be represented using line...

In-Database Learning with Sparse Tensors

In-database analytics is of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features, export the data, convert data format, train models using an external tool, reimport the parameters. It is also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical dat...