sparse-matrix

I'd like to do large-scale regression (linear/logistic) in R with many (e.g. 100k) features, where each example is relatively sparse in the feature space---e.g., ~1k non-zero features per example. It seems like the SparseM package slm should do this, but I'm having difficulty converting from the sparseMatrix format to a slm -friendly format. I have a numeric vector of labels y and a sparseMatrix of features X \in {0,1}. When I try model <- slm(y ~ X) I get the following error: Error in model.frame.default(formula = y ~ X) : invalid type (S4) for variable 'X' presumably because slm wants a

How expensive is it to compute the eigenvalues of a matrix? What is the complexity of the best algorithms? How long might it take in practice if I have a 1000 x 1000 matrix? I assume it helps if the matrix is sparse? Are there any cases where the eigenvalue computation would not terminate? In R , I can compute the eigenvalues as in the following toy example: m<-matrix( c(13,2, 5,4), ncol=2, nrow=2 ) eigen(m, only.values=1) $values [1] 14 3 Does anyone know what algorithm it uses? Are there any other (open-source) packages that compute the eigenvalue? Most of the algorithms for eigen value

I am creating a convolutional sparse autoencoder and I need to convert a 4D matrix full of values (whose shape is [samples, N, N, D] ) into a sparse matrix. For each sample, I have D NxN feature maps. I want to convert each NxN feature map to a sparse matrix, with the maximum value mapped to 1 and all the others to 0. I do not want to do this at run time but during the Graph declaration (because I need to use the resulting sparse matrix as an input to other graph operations), but I do not understand how to get the indices to build the sparse matrix. You can use tf.where and tf.gather_nd to do

I have a large csr_matrix and I am interested in the top ten values and their indices each row. But I did not find a decent way to manipulate the matrix. Here is my current solution and the main idea is to process them row by row: row = csr_matrix.getrow(row_number).toarray()[0].ravel() top_ten_indicies = row.argsort()[-10:] top_ten_values = row[row.argsort()[-10:]] By doing this, the advantages of csr_matrix is not fully used. It's more like a brute force solution. hpaulj I don't see what the advantages of csr format are in this case. Sure, all the nonzero values are collected in one .data

I've got a sparse Matrix in R that's apparently too big for me to run as.matrix() on (though it's not super-huge either). The as.matrix() call in question is inside the svd() function, so I'm wondering if anyone knows a different implementation of SVD that doesn't require first converting to a dense matrix. The irlba package has a very fast SVD implementation for sparse matrices. You can do a very impressive bit of sparse SVD in R using random projection as described in http://arxiv.org/abs/0909.4061 Here is some sample code: # computes first k singular values of A with corresponding singular