The paper reports a method for compressed representation of matrix data on the principles quantum theory. is formalized as complex-valued factorization based standard singular value decomposition. developed approach establishes bridge between methods semantic analysis and models cognition decision. According to theory, real-valued observable quantities are generated by wavefunctions being vecto...

Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better representation. However, current NMF methods do not always generate sparse solutions. In this paper, we propose new method log-norm imposed on the factor matrices enhance sparseness. Mor...

This article proposes an imputation procedure that uses the factors estimated from a tall block along with re-rotated loadings wide to impute missing values in panel of data. Assuming strong factor structure holds for full data and its sub-blocks, it is shown common component can be consistently at four different rates convergence without requiring regularization or iteration. An asymptotic ana...