bhavinthaker commented on a change in pull request #7921: Add three sparse tutorials URL: https://github.com/apache/incubator-mxnet/pull/7921#discussion_r140671190
########## File path: docs/tutorials/sparse/csr.md ########## @@ -0,0 +1,338 @@ + +# CSRNDArray - NDArray in Compressed Sparse Row Storage Format + +Many real world datasets deal with high dimensional sparse feature vectors. Take for instance a recommendation system where the number of categories and users is on the order of millions. The purchase data for each category by user would show that most users only make a few purchases, leading to a dataset with high sparsity (i.e. most of the elements are zeros). + +Storing and manipulating such large sparse matrices in the default dense structure results in wasted memory and processing on the zeros. To take advantage of the sparse structure of the matrix, the `CSRNDArray` in MXNet stores the matrix in [compressed sparse row (CSR)](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_.28CSR.2C_CRS_or_Yale_format.29) format and uses specialized algorithms in operators. +**The format is designed for 2D matrices with a large number of columns, +and each row is sparse (i.e. with only a few nonzeros).** + +## Advantages of Compressed Sparse Row NDArray (CSRNDArray) +For matrices of high sparsity (e.g. ~1% non-zeros), there are two primary advantages of `CSRNDArray` over the existing `NDArray`: Review comment: You may want to introduce both terms, sparsity and density. Say, For matrices of high sparsity, also known as low density (e.g. ~1% non-zeros = ~1% density), there are ... ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
