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

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

scholarly journals Subspace clustering using a symmetric low-rank representation

2017 ◽  
Vol 127 ◽  
pp. 46-57 ◽  
Author(s):  
Jie Chen ◽  
Hua Mao ◽  
Yongsheng Sang ◽  
Zhang Yi
Keyword(s):  
Subspace Clustering ◽  
Low Rank ◽  
Low Rank Representation
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