Results 211 to 220 of about 476,194 (255)
Some of the next articles are maybe not open access.
SIAM Journal on Optimization, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fatih S. Aktas, Mustafa Ç. Pinar
openaire +2 more sources
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fatih S. Aktas, Mustafa Ç. Pinar
openaire +2 more sources
ISPRS Journal of Photogrammetry and Remote Sensing, 2014 As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral ...
Jaime Zabalza +2 more
exaly +2 more sources
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 ...
Hongchuan Yu, Mohammed Bennamoun
openaire +1 more source
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 ...
Hongchuan Yu, Mohammed Bennamoun
openaire +1 more source
2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2020
Heart failure (HF) prediction is a challenging issue in medical informatics and is considered a deadliest disease worldwide. Recent research has been concentrated on features transformation and selection for improved HF prediction. In this study, we search optimal feature extraction algorithm by evaluating the performance of different feature ...
Atiqur Rehman +5 more
openaire +1 more source
Heart failure (HF) prediction is a challenging issue in medical informatics and is considered a deadliest disease worldwide. Recent research has been concentrated on features transformation and selection for improved HF prediction. In this study, we search optimal feature extraction algorithm by evaluating the performance of different feature ...
Atiqur Rehman +5 more
openaire +1 more source
Two-Dimensional Quaternion PCA and Sparse PCA
IEEE Transactions on Neural Networks and Learning Systems, 2019Benefited from quaternion representation that is able to encode the cross-channel correlation of color images, quaternion principle component analysis (QPCA) was proposed to extract features from color images while reducing the feature dimension. A quaternion covariance matrix (QCM) of input samples was constructed, and its eigenvectors were derived to
Xiaolin Xiao, Yicong Zhou
openaire +2 more sources
2007
Histograms are data objects that are commonly used to characterize media objects like image, video, audio etc. Symbolic Data Analysis (SDA) is a field which deals with extracting knowledge and relationship from such complex data objects. The current research scenario of SDA has contributions related to dimensionality reduction of interval kind data ...
P. Nagabhushan, R. Pradeep Kumar
openaire +1 more source
Histograms are data objects that are commonly used to characterize media objects like image, video, audio etc. Symbolic Data Analysis (SDA) is a field which deals with extracting knowledge and relationship from such complex data objects. The current research scenario of SDA has contributions related to dimensionality reduction of interval kind data ...
P. Nagabhushan, R. Pradeep Kumar
openaire +1 more source
, 2018 Best Practices for OnlinePCA.jl against 1.3M Mouse Brain ...
Koki Tsuyuzaki (5864336)
core +3 more sources
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 ...
openaire +1 more source
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 ...
openaire +1 more source
Geodesic PCA versus Log-PCA of Histograms in the Wasserstein Space
SIAM Journal on Scientific Computing, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Elsa Cazelles +4 more
openaire +2 more sources
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 ...
Andreas Weingessel, Kurt Hornik
openaire +2 more sources
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 ...
Andreas Weingessel, Kurt Hornik
openaire +2 more sources