Boolean matrix compressed sensing

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets,...

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Bibliographic Details
Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5
Main Authors: Liu, Qiang, Soleymani, Mahdi, Mahdavifar, Hessam
Format: Conference Proceeding
Language:English
Published: IEEE 06.04.2025
Subjects:
Acoustics
Belief Propagation
Boolean Matrix
Compressed sensing
Decoding
Image coding
Matrix Completion
Sensors
Signal processing
Signal processing algorithms
Sparse matrices
Speech processing
ISSN:2379-190X
Online Access:Get full text
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Summary:In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements.
ISSN:2379-190X
DOI:10.1109/ICASSP49660.2025.10888788