Add to ORKGThis paper introduce simple and general theories of compressed sensing and LASSO. The novelty is that our recovery results do not require the restricted isometry property(RIP). We use the notion of weak RIP that is a natural generalization of RIP. We consider that the proposed results are more useful and flexible for real data analysis in various fields
Abstract Compressed sensing was introduced some ten years ago as an effective way of acquiring signa...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
Abstract. Inspired by significant real-life applications, in particular, sparse phase retrieval and ...
This paper introduce simple and general theories of compressed sensing and LASSO. The novelty is tha...
This paper introduces a new general theory of compressed sensing. We give a natural generalization o...
MI: Global COE Program Education-and-Research Hub for Mathematics-for-IndustryグローバルCOEプログラム「マス･フォア･イ...
The Restricted Isometry Property (RIP) introduced by Candés and Tao is a fundamental property in co...
This paper establishes new sufficient conditions on the restricted isometry property (RIP) for compr...
This paper establishes new sufficient conditions on the restricted isometry property (RIP) for compr...
This paper introduces a simple and very general theory of compressive sensing. In this theory, the s...
This paper introduces a simple and very general theory of compressive sensing. In this theory, the s...
Compressive (or compressed) sensing (CS) is an emerging methodology in computational signal processi...
Compressed sensing (CS) seeks to recover an unknown vector with N entries by making far fewer than N...
Abstract. Compressed Sensing (CS) seeks to recover an unknown vector with N entries by making far fe...
The purpose of this paper is twofold. The first is to point out that the property of uniform recover...
Abstract Compressed sensing was introduced some ten years ago as an effective way of acquiring signa...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
Abstract. Inspired by significant real-life applications, in particular, sparse phase retrieval and ...
This paper introduce simple and general theories of compressed sensing and LASSO. The novelty is tha...
This paper introduces a new general theory of compressed sensing. We give a natural generalization o...
MI: Global COE Program Education-and-Research Hub for Mathematics-for-IndustryグローバルCOEプログラム「マス･フォア･イ...
The Restricted Isometry Property (RIP) introduced by Candés and Tao is a fundamental property in co...
This paper establishes new sufficient conditions on the restricted isometry property (RIP) for compr...
This paper establishes new sufficient conditions on the restricted isometry property (RIP) for compr...
This paper introduces a simple and very general theory of compressive sensing. In this theory, the s...
This paper introduces a simple and very general theory of compressive sensing. In this theory, the s...
Compressive (or compressed) sensing (CS) is an emerging methodology in computational signal processi...
Compressed sensing (CS) seeks to recover an unknown vector with N entries by making far fewer than N...
Abstract. Compressed Sensing (CS) seeks to recover an unknown vector with N entries by making far fe...
The purpose of this paper is twofold. The first is to point out that the property of uniform recover...
Abstract Compressed sensing was introduced some ten years ago as an effective way of acquiring signa...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
Abstract. Inspired by significant real-life applications, in particular, sparse phase retrieval and ...