Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Non-linear dimensionality reduction techniques for unsupervised feature extraction
Pattern Recognition Letters
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Weighted locally linear embedding for dimension reduction
Pattern Recognition
Stable local dimensionality reduction approaches
Pattern Recognition
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Global and local choice of the number of nearest neighbors in locally linear embedding
Pattern Recognition Letters
Dimensionality reduction via compressive sensing
Pattern Recognition Letters
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Dimensionality reduction is a big challenge in many areas. In this research we address the problem of high-dimensional hyperspectral images in which we are aiming to preserve its information quality. This paper introduces a study stability of the non parametric and unsupervised methods of projection and of bands selection used in dimensionality reduction of different noise levels determined with different numbers of data points. The quality criteria based on the norm and correlation are employed obtaining a good preservation of these artificial data in the reduced dimensions. The added value of these criteria can be illustrated in the evaluation of the reduction's performance, when considering the stability of two categories of bands selection methods and projection methods. The performances of the method are verified on artificial data sets for validation. An hybridization for a better stability is proposed in this paper, Band Clustering (BandClust) with Multidimensional Scaling (MDS) for dimensionality reduction. Examples are given to demonstrate the hybridization originality and relevance(BandClust/MDS) of the analysis carried out in this paper.