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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
AlgorithmCholesky decompositionComputer scienceDimensionality reduction

Broader (2)

Computational statisticsMathematical optimization

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2013
2013
Maximum Variance Correction with Application to A* Search
In this paper we introduce Maximum Variance Correction (MVC), which finds largescale feasible solutions to Maximum Variance… 
2013
2013
Utilizing Landmarks in Euclidean Heuristics for Optimal Planning
An important problem in AI is to construct high-quality heuristics for optimal search. Recently, the Euclidean heuristic (EH) has… 
2013
2013
Nonlinear process monitoring by integrating manifold learning with Gaussian process
2012
2012
On a Connection between Maximum Variance Unfolding, Shortest Path Problems and IsoMap
We present an equivalent formulation of the Maximum Variance Unfolding (MVU) approach to nonlinear dimensionality reduction in… 
2011
2011
Fault diagnosis method based on dynamic maximum variance unfolding and one-class support vector machine
In order to analyze nonlinear and dynamic characteristics of industrial process,a new method combining dynamic maximum variance… 
2010
2010
Noise reduction method for nonlinear signal based on maximum variance unfolding and its application to fault diagnosis
A new noise reduction method for nonlinear signal based on maximum variance unfolding (MVU) is proposed. The noisy signal is… 
2009
2009
Video event segmentation and visualisation in non-linear subspace
2008
2008
Scalable semidefinite manifold learning
Maximum variance unfolding (MVU) is among the state of the art manifold learning (ML) algorithms and experimentally proven to be… 
Highly Cited
2006
Highly Cited
2006
Modeling Correspondences for Multi-Camera Tracking Using Nonlinear Manifold Learning and Target Dynamics
Multi-camera tracking systems often must maintain consistent identity labels of the targets across views to recover 3D… 
2005
2005
Semidefinite Embedding Applied to Visualizing Folksonomies CS 6772 Project Proposal
This paper investigates using Semidefinite Embedding (SDE) to visualize data collected from a folksonomy. The del.icio.us social… 
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