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PCA, Kernel PCA and Dimensionality Reduction in Hyperspectral Images
, 2018In this chapter an application of PCA, kernel PCA with their modified versions are discussed in the field of dimensionality reduction of hyperspectral images. Hyperspectral image cube is a set of images from hundreds of narrow and contiguous bands of electromagnetic spectrum from visible to near-infrared regions, which usually contains large amount of ...
Aloke Datta +2 more
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PCA-based Feature Reduction for Hyperspectral Remote Sensing Image Classification
IETE Technical Review, 2020The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential information of land objects through contiguous narrow spectral wavelength bands.
Md. Palash Uddin +2 more
semanticscholar +1 more source
International Journal of Remote Sensing, 2020
Hyperspectral image (HSI) usually holds information of land cover classes as a set of many contiguous narrow spectral wavelength bands. For its efficient thematic mapping or classification, band (feature) reduction strategies through Feature Extraction ...
Md. Palash Uddin +3 more
semanticscholar +1 more source
Hyperspectral image (HSI) usually holds information of land cover classes as a set of many contiguous narrow spectral wavelength bands. For its efficient thematic mapping or classification, band (feature) reduction strategies through Feature Extraction ...
Md. Palash Uddin +3 more
semanticscholar +1 more source
18th International Conference on Pattern Recognition (ICPR'06), 2006
In this paper, we first briefly reintroduce the 1D and 2D forms of the classical principal component analysis (PCA). Then, the PCA technique is further developed and extended to an arbitrary n-dimensional space. Analogous to 1D- and 2D-PCA, the new nD-PCA is applied directly to n-order tensors (n ges 3) rather than 1-order tensors (1D vectors) and 2 ...
Mohammed Bennamoun, Hongchuan Yu
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In this paper, we first briefly reintroduce the 1D and 2D forms of the classical principal component analysis (PCA). Then, the PCA technique is further developed and extended to an arbitrary n-dimensional space. Analogous to 1D- and 2D-PCA, the new nD-PCA is applied directly to n-order tensors (n ges 3) rather than 1-order tensors (1D vectors) and 2 ...
Mohammed Bennamoun, Hongchuan Yu
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Comparison of PCA and 2D-PCA on Indian Faces
2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014), 2014Face recognition is an extensively researched topic by researchers from diverse disciplines. Several unsupervised statistical feature extraction methods have been used in face recognition, out of these in this paper a comparison of the PCA(eigenfaces) and 2D-PCA approaches on Indian Faces has been presented.
Amit Kaul +4 more
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2014
Introduction Two primary techniques for dimension-reducing feature extraction are subspace projection and feature selection . This chapter will explore the key subspace projection approaches, i.e. PCA and KPCA. (i) Section 3.2 provides motivations for dimension reduction by pointing out (1) the potential adverse effect of large feature ...
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Introduction Two primary techniques for dimension-reducing feature extraction are subspace projection and feature selection . This chapter will explore the key subspace projection approaches, i.e. PCA and KPCA. (i) Section 3.2 provides motivations for dimension reduction by pointing out (1) the potential adverse effect of large feature ...
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IEEE Transactions on Neural Networks, 2000
Within the last years various principal component analysis (PCA) algorithms have been proposed. In this paper we use a general framework to describe those PCA algorithms which are based on Hebbian learning. For an important subset of these algorithms, the local algorithms, we fully describe their equilibria, where all lateral connections are set to ...
Kurt Hornik, Andreas Weingessel
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Within the last years various principal component analysis (PCA) algorithms have been proposed. In this paper we use a general framework to describe those PCA algorithms which are based on Hebbian learning. For an important subset of these algorithms, the local algorithms, we fully describe their equilibria, where all lateral connections are set to ...
Kurt Hornik, Andreas Weingessel
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Final Safety Assessment for PCA and Sodium PCA
International Journal of Toxicology, 1999PCA is the cosmetic ingredient term used for the cyclic organic compound known commonly as pyroglutamic acid. Sodium PCA is the sodium salt of PCA. Both are used as hair and skin conditioning agents. These ingredients are recommended to be used in a concentration range of 0.2-4%.
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Robust PCAs and PCA Using Generalized Mean
2017In this chapter, a robust principal component analysis (PCA) is described, which can overcome the problem that PCA is prone to outliers included in training set. Different from the other alternatives which commonly replace \(L_{2}\)-norm by other distance measures, our method alleviates the negative effect of outliers using the characteristic of the ...
Jiyong Oh, Nojun Kwak
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Trends in Cancer, 2018
Reproduced from https://visualsonline.cancer.gov/details.cfm?imageid=11474. Early detection offers a better chance of saving lives from cancer. The National Cancer Institute (NCI) supports research to improve cancer detection in its early stages, when it may be most treatable, and to accurately assess how likely it is for a precancerous growth to ...
Sudhir Srivastava +3 more
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Reproduced from https://visualsonline.cancer.gov/details.cfm?imageid=11474. Early detection offers a better chance of saving lives from cancer. The National Cancer Institute (NCI) supports research to improve cancer detection in its early stages, when it may be most treatable, and to accurately assess how likely it is for a precancerous growth to ...
Sudhir Srivastava +3 more
openaire +3 more sources