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Semidefinite embedding
Known as:
Maximum variance unfolding
, Unfold
Semidefinite embedding (SDE) or maximum variance unfolding (MVU) is an algorithm in computer science that uses semidefinite programming to perform…
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Related topics
Related topics
14 relations
Algorithm
Cholesky decomposition
Computer science
Dimensionality reduction
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Broader (2)
Computational statistics
Mathematical optimization
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Winter 12-15-2014 Applications of Nonlinear Optimization
Yao Xie
2018
Corpus ID: 51938967
OF THE DISSERTATION Applications of Nonlinear Optimization by Xie, Yao Doctor of Philosophy in Mathematics, Washington University…
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2016
2016
Learning with Scalability and Compactness
Wenlin Chen
2016
Corpus ID: 63807760
OF THE DISSERTATION Learning with Scalability and Compactness by Wenlin Chen Doctor of Philosophy in Computer Science Washington…
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2013
2013
Utilizing Landmarks in Euclidean Heuristics for Optimal Planning
Q. Lu
,
Wenlin Chen
,
Yixin Chen
,
Kilian Q. Weinberger
,
Xiaoping Chen
AAAI Conference on Artificial Intelligence
2013
Corpus ID: 3265641
An important problem in AI is to construct high-quality heuristics for optimal search. Recently, the Euclidean heuristic (EH) has…
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2013
2013
Maximum Variance Correction with Application to A* Search
Wenlin Chen
,
Kilian Q. Weinberger
,
Yixin Chen
International Conference on Machine Learning
2013
Corpus ID: 17290441
In this paper we introduce Maximum Variance Correction (MVC), which finds largescale feasible solutions to Maximum Variance…
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2012
2012
Supervised maximum variance unfolding and its application to gait recognition
Wang Xuqi
,
Zhang Shanwen
2012
Corpus ID: 58958220
2010
2010
Noise reduction method for nonlinear signal based on maximum variance unfolding and its application to fault diagnosis
Yun Zhang
,
Benwei Li
2010
Corpus ID: 76649788
A new noise reduction method for nonlinear signal based on maximum variance unfolding (MVU) is proposed. The noisy signal is…
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2010
2010
Probabilistic Spectral Dimensionality Reduction
Neil D. Lawrence
2010
Corpus ID: 630600
We introduce a new perspective on spectral dimensionality reduction which views these methods as Gaussian random fields (GRFs…
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2008
2008
Learning Isometric Separation Maps
N. Vasiloglou
,
Alexander G. Gray
,
David V. Anderson
IEEE International Workshop on Machine Learning…
2008
Corpus ID: 1998133
Maximum Variance Unfolding (MVU) and its variants have been very successful in embedding data-manifolds in lower dimensional…
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2006
2006
Hierarchical Directed Spectral Graph Partitioning MS&E 337 - Information Networks
D. Gleich
2006
Corpus ID: 18762938
In this report, we examine the generalization of the Laplacian of a graph due to Fan Chung. We show that Fan Chung’s…
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2005
2005
Semidefinite Embedding Applied to Visualizing Folksonomies CS 6772 Project Proposal
B. Shaw
2005
Corpus ID: 5161178
This paper investigates using Semidefinite Embedding (SDE) to visualize data collected from a folksonomy. The del.icio.us social…
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