Active-Set Newton Algorithm for Overcomplete Non-Negative Representations of Audio
نویسندگان
چکیده
منابع مشابه
An EM algorithm for learning sparse and overcomplete representations
An expectation-maximization (EM) algorithm for learning sparse and overcomplete representations is presented in this paper. We show that the estimation of the conditional moments of the posterior distribution can be accomplished by maximum a posteriori estimation. The approximate conditional moments enable the development of an EM algorithm for learning the overcomplete basis vectors and inferr...
متن کاملLearning Overcomplete Representations
In an overcomplete basis, the number of basis vectors is greater than the dimensionality of the input, and the representation of an input is not a unique combination of basis vectors. Overcomplete representations have been advocated because they have greater robustness in the presence of noise, can be sparser, and can have greater flexibility in matching structure in the data. Overcomplete code...
متن کاملA Class of Active-Set Newton Methods for Mixed ComplementarityProblems
Based on the identification of indices active at a solution of the mixed complementarity problem (MCP), we propose a class of Newton methods for which local superlinear convergence holds under extremely mild assumptions. In particular, the error bound condition needed for the identification procedure and the nondegeneracy condition needed for the convergence of the resulting Newton method are i...
متن کاملAn Active Set Newton Algorithm for Large-Scale Nonlinear Programs with Box Constraints
A new algorithm for large-scale nonlinear programs with box constraints is introduced. The algorithm is based on an eecient identiication technique of the active set at the solution and on a nonmonotone stabilization technique. It possesses global and superlinear convergence properties under standard, mild assumptions. A new technique for generating test problems with known characteristics is a...
متن کاملEstimation of Sparse Non-negative Sources from Noisy Overcomplete
In this paper, a new algorithm for estimating sparse non-negative sources from a set of noisy linear mixtures is proposed. In particular, difficult situations with high noise levels and more sources than sensors (underdetermined case) are considered. It is shown that, when sources are very sparse in time and overlapped at some locations, they can be recovered even with very low SNR and by using...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Audio, Speech, and Language Processing
سال: 2013
ISSN: 1558-7916,1558-7924
DOI: 10.1109/tasl.2013.2263144