.png){kind=icon}
.png){kind=icon}
Return
Backward sequential elimination for sparse vector subset selection
.png){kind=icon}
PRE
AI
DOI：10.1016/S0165-1684(01)00064-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Selection of a subset of vectors from a larger dictionary of vectors arises in a wide variety of application areas. This problem is known to be NP-hard and many algorithms have been proposed for the suboptimal solution of this problem. The focus of this paper is the development of a backward sequential elimination algorithm wherein. starting from the full dictionary, elements are deleted until a subset of a desired size is obtained. In contrast to previous formulations, we start with an overcomplete dictionary of vectors which is often the problem faced in a signal representation context. Once enough vectors have been deleted to give a complete system.. the algorithm is modified to allow further deletion of vectors, In addition, the derived algorithm gives access to the coefficients associated with each vector in representing the signal. This allows us to experiment with different criteria, including entropy-based and p-norm criteria. for selection of the vector to be deleted in each iteration. There is also the flexibility to combine criteria or to switch between criteria at a given stage of the algorithm, Following a series of simulations on a test-case system., we are able to conclude that the p-norm. close to 1 performs best while the system considered is overcomplete. A minimum representation error criterion gives the best results once the system considered becomes undercomplete. The performance of the algorithm is also compared to that of forward selection algorithms on the test-case dictionary. (C) 2001 Elsevier Science B.V. All rights reserved.
Keywords:
subset selection
sparsity
backward elimination
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.9K
Citations:
1.7W
Organization
No organization information available