A Frame Construction and a Universal Distortion Bound for Sparse Representations
By Mehmet Akçakaya, Vahid Tarokh. 2008
104 citations recorded by the source corpus.
Subfields: Applied Mathematics, Computational Mechanics, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Mathematics
- Suhas N. Diggavi
- Babak Hassibi
- Farzad Parvaresh
- Deepesh Data
- Mihailo Stojnic
- Linqi Song
- Niall P. Hurley
- Scott T. Rickard
In Computational Mechanics
- Deepesh Data
- Linqi Song
- Suhas N. Diggavi
- Babak Hassibi
- Farzad Parvaresh
- Mihailo Stojnic
- Niall P. Hurley
- Scott T. Rickard
In Computer Vision and Pattern Recognition
- Suhas N. Diggavi
- Babak Hassibi
- Farzad Parvaresh
- Deepesh Data
- Mihailo Stojnic
- Linqi Song
- Niall P. Hurley
- Scott T. Rickard
Citations in the corpus
- Data Encoding for Byzantine-Resilient Distributed Optimization (2019, methodology): “We define F to be the following Vandermonde matrix (where k < m ): Below we state a result (specialized to reals) from [AT08].”
- On the Reconstruction of Block-Sparse Signals With an Optimal Number of Measurements (2008, background): “Furthermore, we will demonstrate how (3) can be adapted for practical considerations.”
- Data Encoding Methods for Byzantine-Resilient Distributed Optimization (2019, methodology): “In particular, we can use a 2 t × m Vandermonde matrix along with the Reed-Solomon decoding, which can correct up to t errors and has decoding complexity of O ( m 2 ) [23].”
- Data Encoding for Byzantine-Resilient Distributed Gradient Descent (2018, methodology): “Akçakaya and Tarokh [26] constructed a complex analogue of the Reed-Solomon codes from k × m Vandermonde matrices F and gave an O(m(2)) time algorithm for exactly reconstructing e from f = Fe, provided |supp(e)| ≤ k/2.”
- Comparing Measures of Sparsity (2008, methodology): “…and techniques such as matrix factorization [7], signal recovery/extraction [8], denoising [9], [10], compressed sensing [11], dictionary learning [12], signal representation [13], [14], support vector machines [15], sampling theory [16], [17] and source separation/localization [18], [19].”
- Byzantine-Tolerant Distributed Coordinate Descent (2019, background): “, we can recover e from Fe, provided e has at most k/2 non-zero entries) and has O(m(2)) decoding complexity [18].”