scholarly journals Infrared Weak-Small Targets Fusion Based on Latent Low-Rank Representation and DWT

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 112681-112692
Author(s):  
Xiaozhu Wang ◽  
Jianfei Yin ◽  
Kai Zhang ◽  
Shayi Li ◽  
Jie Yan
Keyword(s):  
Low Rank ◽  
Low Rank Representation ◽  
Small Targets

scholarly journals Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function

2020 ◽  
Vol 10 ◽  
Author(s):  
Conghai Lu ◽  
Juan Wang ◽  
Jinxing Liu ◽  
Chunhou Zheng ◽  
Xiangzhen Kong ◽  
...  
Keyword(s):  
Score Function ◽  
Low Rank ◽  
Low Rank Representation ◽  
Regularized Method

scholarly journals Robust Principal Component Analysis-Based Infrared Small Target Detection

2019 ◽  
Vol 33 ◽  
pp. 9925-9926
Author(s):  
Qiwei Chen ◽  
Cheng Wu ◽  
Yiming Wang
Keyword(s):  
Iteration Process ◽  
Principal Component ◽  
Component Analysis ◽  
Low Rank ◽  
Small Target ◽  
Frame Sequence ◽  
Augmented Lagrange Multiplier ◽  
Small Targets ◽  
Infrared Small Target ◽  
Augmented Lagrange Multiplier Method

A method based on Robust Principle Component Analysis (RPCA) technique is proposed to detect small targets in infrared images. Using the low rank characteristic of background and the sparse characteristic of target, the observed image is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA. The infrared small target is extracted from the single-frame image or multi-frame sequence. In order to get more efficient algorithm, the iteration process in the augmented Lagrange multiplier method is improved. The simulation results show that the method can detect out the small target precisely and efficiently.

Computing Robust Principal Components by A* Search

2018 ◽  
Vol 27 (07) ◽  
pp. 1860013 ◽  
Author(s):  
Swair Shah ◽  
Baokun He ◽  
Crystal Maung ◽  
Haim Schweitzer
Keyword(s):  
State Of The Art ◽  
Principal Component ◽  
Low Rank ◽  
A Algorithm ◽  
Running Time ◽  
Current State ◽  
Dimensionality Reduction Technique ◽  
Related Variant ◽  
Low Rank Representation ◽  
The Cost

Principal Component Analysis (PCA) is a classical dimensionality reduction technique that computes a low rank representation of the data. Recent studies have shown how to compute this low rank representation from most of the data, excluding a small amount of outlier data. We show how to convert this problem into graph search, and describe an algorithm that solves this problem optimally by applying a variant of the A* algorithm to search for the outliers. The results obtained by our algorithm are optimal in terms of accuracy, and are shown to be more accurate than results obtained by the current state-of-the- art algorithms which are shown not to be optimal. This comes at the cost of running time, which is typically slower than the current state of the art. We also describe a related variant of the A* algorithm that runs much faster than the optimal variant and produces a solution that is guaranteed to be near the optimal. This variant is shown experimentally to be more accurate than the current state-of-the-art and has a comparable running time.

scholarly journals Accounting for non-genetic factors by low-rank representation and sparse regression for eQTL mapping

2013 ◽  
Vol 29 (8) ◽  
pp. 1026-1034 ◽  
Author(s):  
Can Yang ◽  
Lin Wang ◽  
Shuqin Zhang ◽  
Hongyu Zhao
Keyword(s):  
Genetic Factors ◽  
Low Rank ◽  
Eqtl Mapping ◽  
Sparse Regression ◽  
Low Rank Representation
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