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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.
2017
2017
Multi-view Manifold Learning for Media Interestingness Prediction
Media interestingness prediction plays an important role in many real-world applications and attracts much research attention… 
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
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… 
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… 
2010
2010
Learning Video Manifold for Segmenting Crowd Events and Abnormality Detection
This paper addresses the problem of analyzing video events in crowded scenes. A novel manifold learning method is proposed to… 
2007
2007
Diffusion Maps and Geometric Harmonics for Automatic Target Recognition (ATR). Volume 2. Appendices
Abstract : Geometric harmonics provides a framework for taking data in high-dimensional measurement spaces and embedding them in… 
2006
2006
Analyzing Human Movements from Silhouettes Using Manifold Learning
  • Liang WangD. Suter
  • IEEE International Conference on Video and Signal…
  • 2006
  • Corpus ID: 1120094
A novel method for learning and recognizing sequential image data is proposed, and promising applications to vision-based human… 
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…