$L^1$-convergence of greedy algorithm by generalized Walsh system
نویسندگان
چکیده
منابع مشابه
Fast Implementation of l1-Greedy Algorithm
We present an algorithm for finding sparse solutions of the system of linear equations Ax = b with the rectangular matrix A of size n×N, where n < N. The algorithm basic constructive block is one iteration of the standard interior-point linear programming algorithm. To find the sparse representation we modify (reweight) each iteration in the spirit of [12]. However, the weights are selected acc...
متن کاملNumerical Studies of the Generalized l1 Greedy Algorithm for Sparse Signals
The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the gen...
متن کاملGeneralized Greedy Algorithm for Shortest Superstring
In the primitive greedy algorithm for shortest superstring, if a pair of strings with maximum overlap picked out, they are subsequently merged. In this paper, we introduce the concept of optimal set and generalize the primitive greedy algorithm. The generalized algorithm can be reduced to the primitive greedy algorithm if the relative optimal set is empty. Consequently, the new algorithm achiev...
متن کاملA Greedy Algorithm to Construct L1 Graph with Ranked Dictionary
L1 graph is an effective way to represent data samples in many graph-oriented machine learning applications. Its original construction algorithm is nonparametric, and the graphs it generates may have high sparsity. Meanwhile, the construction algorithm also requires many iterative convex optimization calculations and is very time-consuming. Such characteristics would severely limit the applicat...
متن کاملConvergence rate of the semi-supervised greedy algorithm
This paper proposes a new greedy algorithm combining the semi-supervised learning and the sparse representation with the data-dependent hypothesis spaces. The proposed greedy algorithm is able to use a small portion of the labeled and unlabeled data to represent the target function, and to efficiently reduce the computational burden of the semi-supervised learning. We establish the estimation o...
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: Banach Journal of Mathematical Analysis
سال: 2012
ISSN: 1735-8787
DOI: 10.15352/bjma/1337014675