Matrix Computations—Euclidean and Non-Euclidean Domains

1985 ◽  
pp. 133-145
Author(s):  
E. V. Krishnamurthy
Keyword(s):  
Matrix Computations ◽  
Euclidean Domains

Parallel Matrix Computations.

1986 ◽  
Author(s):  
G. W. Stewart ◽  
Dianne P. O'Leary
Keyword(s):  
Matrix Computations

Sequential and Parallel Matrix Computations.

1985 ◽  
Author(s):  
Biswa N. Datta
Keyword(s):  
Matrix Computations

Large Sparse Stable Matrix Computations

1990 ◽  
Author(s):  
Alex Pothen ◽  
Jesse L. Barlow
Keyword(s):  
Matrix Computations ◽  
Stable Matrix

Matrix Computations

2005 ◽  
Author(s):  
John H. Maindonald ◽  
Gordon K. Smyth
Keyword(s):  
Matrix Computations

Conference on Matrix Computations (Abstracts)

1957 ◽  
Vol 4 (4) ◽  
pp. 520-523
Author(s):  
Anonymous
Keyword(s):  
Matrix Computations

scholarly journals A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3627 ◽  
Author(s):  
Yi Zhang ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yan Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Sparse Representation ◽  
Parallel Algorithm ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Detection Methods ◽  
Matrix Computations ◽  
Hyperspectral Image Processing ◽  
Mapreduce Model

Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA’s efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.

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