Adaptive Sampling for Nonlinear Dimensionality Reduction Based on Manifold Learning

2017 ◽  
pp. 255-269
Author(s):  
Thomas Franz ◽  
Ralf Zimmermann ◽  
Stefan Görtz
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Adaptive Sampling ◽  
Nonlinear Dimensionality Reduction

Manifold Transfer Subspace Learning (MTSL) for Applications in Aided Target Recognition

2017 ◽  
Vol 5 (3) ◽  
pp. 15-32
Author(s):  
Olga Mendoza-Schrock ◽  
Mateen M. Rizki ◽  
Vincent J. Velten
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Target Recognition ◽  
Subspace Learning ◽  
Heat Diffusion ◽  
Nonlinear Dimensionality Reduction ◽  
Cross Domain ◽  
Learning Technique ◽  
Data Collections ◽  
New System

This article describes how transfer subspace learning has recently gained popularity for its ability to perform cross-dataset and cross-domain object recognition. The ability to leverage existing data without the need for additional data collections is attractive for monitoring and surveillance technology, specifically for aided target recognition applications. Transfer subspace learning enables the incorporation of sparse and dynamically collected data into existing systems that utilize large databases. Manifold learning has also gained popularity for its success at dimensionality reduction. In this contribution, Manifold learning and transfer subspace learning are combined to create a new system capable of achieving high target recognition rates. The manifold learning technique used in this contribution is diffusion maps, a nonlinear dimensionality reduction technique based on a heat diffusion analogy. The transfer subspace learning technique used is Transfer Fisher's Linear Discriminative Analysis. The new system, manifold transfer subspace learning, sequentially integrates manifold learning and transfer subspace learning. In this article, the ability of the new techniques to achieve high target recognition rates for cross-dataset and cross-domain applications is illustrated using a variety of diverse datasets.

scholarly journals Multiple Manifold Learning by Nonlinear Dimensionality Reduction

2011 ◽  
pp. 206-213 ◽  
Author(s):  
Juliana Valencia-Aguirre ◽  
Andrés Álvarez-Meza ◽  
Genaro Daza-Santacoloma ◽  
Carlos Acosta-Medina ◽  
César Germán Castellanos-Domínguez
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Nonlinear Dimensionality Reduction

Classification Criterion Based Neighborhood Optimization Method on Laplacian Eigenmaps

2011 ◽  
Vol 403-408 ◽  
pp. 2679-2682
Author(s):  
Quan Sheng Jiang ◽  
Su Ping Li
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Parameter Optimization ◽  
Learning Algorithms ◽  
Optimization Method ◽  
Classification Performance ◽  
Nonlinear Dimensionality Reduction ◽  
Classification Criterion ◽  
Criterion Function ◽  
Laplacian Eigenmaps

Manifold learning algorithms are nonlinear dimensionality reduction algorithms rising in recent years. Laplacian Eigenmaps is a typical manifold learning algorithms. Aim to the difficulty of selecting neighborhood parameter on the algorithm, a neighborhood parameter optimization method based on classification criterion is proposed in the paper. From the point of the classification performance, the classification criterion function is constructed to reflect the distance of within-class and between-class. The optimization of the neighborhood is obtained according to the minimum of the criterion function. The experimental results on IRIS validate the optimization of the neighborhood and the effectiveness of feature classification.

scholarly journals Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering

2014 ◽  
Vol 74 (3) ◽  
pp. 868-878 ◽  
Author(s):  
Guang Yang ◽  
Felix Raschke ◽  
Thomas R. Barrick ◽  
Franklyn A. Howe
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Mr Spectroscopy ◽  
Unsupervised Clustering ◽  
Nonlinear Dimensionality Reduction
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