Journal ArticleDOI
Greedy Basis Pursuit
P.S. Huggins,Steven W. Zucker +1 more
TLDR
G greedy basis pursuit (GBP), a new algorithm for computing sparse signal representations using overcomplete dictionaries, is introduced and experiments show that GBP can provide a fast alternative to standard linear programming approaches to basis pursuit.Abstract:
We introduce greedy basis pursuit (GBP), a new algorithm for computing sparse signal representations using overcomplete dictionaries. GBP is rooted in computational geometry and exploits equivalence between minimizing the l1-norm of the representation coefficients and determining the intersection of the signal with the convex hull of the dictionary. GBP unifies the different advantages of previous algorithms: like standard approaches to basis pursuit, GBP computes representations that have minimum l1-norm; like greedy algorithms such as matching pursuit, GBP builds up representations, sequentially selecting atoms. We describe the algorithm, demonstrate its performance, and provide code. Experiments show that GBP can provide a fast alternative to standard linear programming approaches to basis pursuit.read more
Citations
More filters
Journal ArticleDOI
False data injection attacks against state estimation in electric power grids
TL;DR: In this article, a new class of attacks, called false data injection attacks, against state estimation in electric power grids is presented and analyzed, under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations.
Proceedings ArticleDOI
False data injection attacks against state estimation in electric power grids
TL;DR: A new class of attacks, called false data injection attacks, against state estimation in electric power grids are presented, showing that an attacker can exploit the configuration of a power system to launch such attacks to successfully introduce arbitrary errors into certain state variables while bypassing existing techniques for bad measurement detection.
Journal ArticleDOI
Subset Selection in Regression
TL;DR: Chapman and Miller as mentioned in this paper, Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40, 1990) and Section 5.8.
Journal ArticleDOI
Recovering Quantitative Remote Sensing Products Contaminated by Thick Clouds and Shadows Using Multitemporal Dictionary Learning
TL;DR: This paper proposes two multitemporal dictionary learning algorithms, expanding on their KSVD and Bayesian counterparts, to make better use of the temporal correlations of quantitative data contaminated by thick clouds and shadows.
Journal ArticleDOI
A Review of Sparse Recovery Algorithms
TL;DR: A comprehensive study and a state-of-the-art review of compressive sensing theory algorithms used in imaging, radar, speech recognition, and data acquisition and some open research challenges are presented.
References
More filters
Book
Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Journal ArticleDOI
Atomic Decomposition by Basis Pursuit
TL;DR: Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions.
Journal ArticleDOI
Matching pursuits with time-frequency dictionaries
Stéphane Mallat,Zhifeng Zhang +1 more
TL;DR: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions, chosen in order to best match the signal structures.
Journal ArticleDOI
$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.