Dictionary Identification—Sparse Matrix-Factorization via $\ell_1$-Minimization

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Dictionary Identification - Sparse Matrix-Factorisation via $\ell_1$-Minimisation

This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via `1-minimisation. The problem can also be seen as factorising a d × N matrix Y = (y1 . . . yN ), yn ∈ R of training signals into a d × K dictionary matrix Φ and a K × N coefficient matrix X = (x1 . . . xN ), xn ∈ R , which is sparse. The exact question studied here is when a di...

متن کامل

Remote sensing via $\ell_1$ minimization

We consider the problem of detecting the locations of targets in the far field by sending probing signals from an antenna array and recording the reflected echoes. Drawing on key concepts from the area of compressive sensing, we use an `1-based regularization approach to solve this, in general ill-posed, inverse scattering problem. As common in compressive sensing, we exploit randomness, which ...

متن کامل

Approximate Nonnegative Matrix Factorization via Alternating Minimization

In this paper we consider the Nonnegative Matrix Factorization (NMF) problem: given an (elementwise) nonnegative matrix V ∈ R + find, for assigned k, nonnegative matrices W ∈ R + and H ∈ R k×n + such that V = WH . Exact, non trivial, nonnegative factorizations do not always exist, hence it is interesting to pose the approximate NMF problem. The criterion which is commonly employed is I-divergen...

متن کامل

Tensor Factorization via Matrix Factorization

Tensor factorization arises in many machinelearning applications, such knowledge basemodeling and parameter estimation in latentvariable models. However, numerical meth-ods for tensor factorization have not reachedthe level of maturity of matrix factorizationmethods. In this paper, we propose a newmethod for CP tensor factorization that usesrandom projections to ...

متن کامل

Dictionary Learning for Massive Matrix Factorization

Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising. Its applicability to large datasets has been addressed with online and randomized methods, that reduce the complexity in one of the matrix dimension, but not in both of them. In this paper, we tackle very large matrices in both dimensions. ...

متن کامل

ذخیره 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 Information Theory

سال: 2010

ISSN: 0018-9448,1557-9654

DOI: 10.1109/tit.2010.2048466