Performance measure for sparse recovery algorithms in compressed sensing perspective

V. Vivekanand; Deepak Mishra

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Performance measure for sparse recovery algorithms in compressed sensing perspective

机译：Performance measure for sparse recovery algorithms in compressed sensing perspective

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Abstract The sparse signal recovery is of great interest in compressed sensed data recovery. Many sparse recovery algorithms were developed in the last decade. However, selection of an appropriate recovery algorithm is an important matter of concern in many applications. The recovery algorithms are generally compared in terms of computational complexity, computational time, recovery probability and recovery precision. Typically, absolute Mean Squared Error (MSE) and relative MSE are used to compare the recovery precision of various sparse recovery algorithms. However, these two metric alone may not qualify to assess all algorithms. This paper presents an algorithm evaluation strategy by ranking the algorithms concerning an observable similarity between the original and reconstructed signal. We aim to propose a recovery similarity measure and an empirically defined factor to compare the performance measure of sparse recovery algorithms.

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来源
《International Journal of Data Science and Analytics》 |2023年第4期|391-406|共16页
作者
V. Vivekanand; Deepak Mishra;
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作者单位

Vikram Sarabhai Space Centre, Indian Space Research Organisation;

Indian Institute of Space Science and Technology;

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原文格式 PDF
正文语种英语
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关键词
Compressed sensing; Sparse recovery; Performance measure; Performance metric; Algorithm evaluation;

Performance measure for sparse recovery algorithms in compressed sensing perspective

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