It is well‐known that suitably designed sparse arrays (with large and contiguous difference co‐arrays) are able to localize more sources than sensors in the pas ...

In sparse array signal processing, all the equivalent virtual signals vectorized from the covariance matrix are contained in the single-snapshot measurement vector. However, spatial smoothing ...

This snippet works fine because the sparse y is converted to a numpy array. from sklearn import linear_model, multioutput from scipy.sparse import csr_matrix x = csr_matrix((13, 5)) y = csr_matrix( ...

Vectors and matrices fall under the tensor category. Tensors are multidimensional arrays of numbers. Vectors are flat and have one dimension, wheres matrices are two-dimensional. Saman Amarasinghe, ...

Describe the bug TypeError: A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array. is thrown when passing a sparse matrix to the fit method Steps ...

Tony Cai, Weidong Liu, Yin Xia, Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings, Journal of the American Statistical Association, Vol. 108, No. 501 ...