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Nonlinear dimensionality reduction

Known as: Non-linear dimensionality reduction, Locally linear embeddings, Locally linear embedding 
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to… 
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Related topics

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AutoencoderBackpropagationCurse of dimensionalityDegree matrix

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2016
2016
Multi-atlas segmentation using manifold learning with deep belief networks
This paper proposes a novel combination of manifold learning with deep belief networks for the detection and segmentation of left… 
2016
2016
Thought Chart: Tracking Dynamic EEG Brain Connectivity with Unsupervised Manifold Learning
Assuming that the topological space containing all possible brain states forms a very high-dimensional manifold, this paper… 
2014
2014
Manifold Learning for Jointly Modeling Topic and Visualization
Classical approaches to visualization directly reduce a document's high-dimensional representation into visualizable two or… 
2014
2014
Generalized kernel framework for unsupervised spectral methods of dimensionality reduction
This work introduces a generalized kernel perspective for spectral dimensionality reduction approaches. Firstly, an elegant… 
Review
2012
Review
2012
A SURVEY OF DIMENSIONALITY REDUCTION AND CLASSIFICATION METHODS
Dimensionality Reduction is usually achieved on the feature space by adopting any one of the prescribed methods that fall under… 
2012
2012
Selecting Key Poses on Manifold for Pairwise Action Recognition
In action recognition, bag of visual words based approaches have been shown to be successful, for which the quality of codebook… 
2011
2011
Estimating patient-specific shape prior for medical image segmentation
Image segmentation is one of the key problems in medical image analysis. This paper presents a new statistical shape model for… 
2007
2007
A new nonlinear dimensionality reduction method with application to hyperspectral image analysis
In this paper, we propose a new nonlinear dimensionality reduction method by combining Locally Linear Embedding (LLE) with… 
Highly Cited
2005
Highly Cited
2005
Computer Vision for Biomedical Image Applications, First International Workshop, CVBIA 2005, Beijing, China, October 21, 2005, Proceedings
2002
2002
Using Thousands of Images of an Object
In this paper we consider the analysis of thousands of unorganized , low resolution images of an object. With very low resolution…