NDLI: Intrinsic dimensionality estimation and dimensionality reduction through scale space filtering

Content Provider	IEEE Xplore Digital Library
Author	Karantzalos, K.
Copyright Year	2009
Description	Author affiliation: Laboratoire de Mathematiques Appliquees aux Systemes (MAS), Ecole Centrale de Paris, Chatenay-Malabry, France (Karantzalos, K.)
Abstract	Dimensionality reduction techniques are designed to exploit the fact that most high-dimensional datasets from the real world do not uniformly fill the hyperspaces in which they are represented but instead their distributions usually concentrate to nonlinear manifolds of lower intrinsic dimensions. However, when these techniques are applied directly to the initial degraded and noisy data, the assumptions on the possible statistical separation of real world classes do not, in the general case, hold. In this paper, we argue that scale space filtering, by denoising and simplifying effectively the initial dataset, ameliorate the way the properties of our observations are been encoded, strengthening, thus, the assumptions on the possible statistical separation of real world classes. Experimental results on real hyperspectral datasets demonstrate that appropriate vector-valued scale space filtering significantly contributes to the intrinsic dimension estimation and dimensionality reduction of high dimensional datasets.
Starting Page	1
Ending Page	6
File Size	482032
Page Count	6
File Format	PDF
ISBN	9781424432974
DOI	10.1109/ICDSP.2009.5201196
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-05
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Hyperspectral imaging Noise reduction Hyperspectral sensors Degradation Anisotropic magnetoresistance Data processing Hypercubes Multidimensional systems Smoothing methods Denoising Multidimensional Data Hyperspectral Imaging Manifold Learning Mathematical Morphology Levelings Anisotropic Diffusion Image Simplification
Content Type	Text
Resource Type	Article

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