scholarly journals Robust temporal low‐rank representation for traffic data recovery via fused lasso

2021 ◽  
Vol 15 (2) ◽  
pp. 175-186
Author(s):  
Ruyong Mao ◽  
Zhengyu Chen ◽  
Guobing Hu
Keyword(s):  
Data Recovery ◽  
Low Rank ◽  
Traffic Data ◽  
Fused Lasso ◽  
Low Rank Representation

scholarly journals Edge Computing-Empowered Large-Scale Traffic Data Recovery Leveraging Low-Rank Theory

2020 ◽  
Vol 7 (4) ◽  
pp. 2205-2218 ◽  
Author(s):  
Chaocan Xiang ◽  
Zhao Zhang ◽  
Yuben Qu ◽  
Dongyu Lu ◽  
Xiaochen Fan ◽  
...  
Keyword(s):  
Large Scale ◽  
Edge Computing ◽  
Data Recovery ◽  
Low Rank ◽  
Traffic Data

Tensor Low-rank Representation for Data Recovery and Clustering

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Pan Zhou ◽  
Canyi Lu ◽  
Jiashi Feng ◽  
Zhouchen Lin ◽  
Shuicheng Yan
Keyword(s):  
Data Recovery ◽  
Low Rank ◽  
Low Rank Representation

Low-rank representation based traffic data completion method

2016 ◽  
Author(s):  
Rong Du ◽  
Yong Zhang ◽  
Boyue Wang ◽  
Hao Liu ◽  
Guanglei Qi ◽  
...  
Keyword(s):  
Low Rank ◽  
Traffic Data ◽  
Data Completion ◽  
Low Rank Representation

Low-Rank Representation of Nearest Neighbor Posterior Probabilities to Enhance DNN Based Acoustic Modeling

2016 ◽  
Author(s):  
Gil Luyet ◽  
Pranay Dighe ◽  
Afsaneh Asaei ◽  
Hervé Bourlard
Keyword(s):  
Nearest Neighbor ◽  
Acoustic Modeling ◽  
Low Rank ◽  
Posterior Probabilities ◽  
Low Rank Representation

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 Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation

2021 ◽  
pp. 1-1
Author(s):  
Yuheng Jia ◽  
Hui Liu ◽  
Junhui Hou ◽  
Sam Kwong ◽  
Qingfu Zhang
Keyword(s):  
Spectral Clustering ◽  
Low Rank ◽  
Low Rank Representation

Spectral-Difference Low-Rank Representation Learning for Hyperspectral Anomaly Detection

2021 ◽  
pp. 1-14
Author(s):  
Xiangrong Zhang ◽  
Xiaoxiao Ma ◽  
Ning Huyan ◽  
Jing Gu ◽  
Xu Tang ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Representation Learning ◽  
Low Rank ◽  
Spectral Difference ◽  
Low Rank Representation

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.

Sign in / Sign up

Export Citation Format

Share Document