Nonnegative matrix factorization (NMF) has been successfully applied in several data mining tasks. Recently, there is an increasing interest the acceleration of NMF, due to its high cost on large matrices. On other hand, privacy issue NMF over federated worthy attention, since prevalently image and text analysis which may involve leveraging (e.g, medical record) across parties (e.g., hospitals)...

Amethodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in other techniques such as principal compone...

A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in other techniques such as principal compon...

Dedicated to Hans Schneider on the occasion of his seventieth birthday. Abstract. Let A be an entrywise nonnegative n n matrix and let sk := trace (A) (k = 1;2; : : :). It is shown that if n is odd and s1 = 0, then (n 1)s4 s22. The result is applied to show that (3; 1 2 (1 p17); 2; 2) is not the spectrum of a nonnegative 5 5 matrix while (3; 1 2 (1 p 17); 2; 2;0) is the spectrum of a nonnegativ...

A matrix is said to have the Perron-Frobenius property if its spectral radius is an eigenvalue with a corresponding nonnegative eigenvector. Matrices having this and similar properties are studied in this paper as generalizations of nonnegative matrices. Sets consisting of such generalized nonnegative matrices are studied and certain topological aspects such as connectedness and closure are pro...