Working-with-sparse-matrices-in-MATLAB-and-R- Sometimes a researcher might find themselves needing to build an extremely large matrix that only contains a small number of non-zero values.

Notice that repeated entries are added together. It is faster to set multiple values at once using add_vals. Nevertheless, this implementation should perform better than Matlab's sparse during a loop ...

Learn what are the benefits and limitations of using sparse matrices in numerical linear algebra, and how to handle them efficiently and accurately.

However, it might be more reasonable to require both W and H are sparse when trying to learn useful features from a database of images. In this paper, we propose a co-sparse non-negative matrix ...

Summary <p>This chapter examines the seven variants of sparse matrices implemented in the Scipy library. These seven variants include: coordinate sparse matrix, compressed sparse column matrix, ...

Fuzzy systems applying a sparse rule base and a fuzzy rule interpolation based reasoning method are popular solutions in cases with partial knowledge of the modeled area or cases when the full ...

We introduce a new sparse sliced inverse regression estimator called Cholesky matrix penalization, and its adaptive version, for achieving sparsity when estimating the dimensions of a central subspace ...