Results 261 to 270 of about 1,131,696 (312)

Two-Dimensional Quaternion PCA and Sparse PCA

IEEE Transactions on Neural Networks and Learning Systems, 2019
Benefited 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   +3 more sources

Performance Analysis of PCA, Sparse PCA, Kernel PCA and Incremental PCA Algorithms for Heart Failure Prediction

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, Aurangzeb Khan, Muhammad Akhtar Ali, Muhammad Umair Khan, Shafqat Ullah Khan, Liaqat Ali   +5 more
openaire   +2 more sources

PCA Sparsified

SIAM Journal on Optimization, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fatih S. Aktaş, Mustafa Ç. Pinar
openaire   +1 more source

Forecasting crude oil prices: A scaled PCA approach

Energy Economics, 2021
In this paper, we employ a novel dimension reduction approach, the scaled principal component analysis (s-PCA), to improve the oil price predictability with technical indicators.
Mengxi He, Yaojie Zhang, Danyan Wen, Yudong Wang   +3 more
semanticscholar   +1 more source

Groundwater pollution source identification and apportionment using PMF and PCA-APCA-MLR receptor models in a typical mixed land-use area in Southwestern China.

Science of the Total Environment, 2020
The quality of groundwater in a region is regarded as a function of natural and anthropogenic factors. Receptor models have advantages in source identification and source apportionment by testing the physicochemical properties of receptor samples and ...
Han Zhang, Siqian Cheng, Hongfei Li, K. Fu, Yi Xu   +4 more
semanticscholar   +1 more source

PCA-based Feature Reduction for Hyperspectral Remote Sensing Image Classification

IETE Technical Review, 2020
The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential information of land objects through contiguous narrow spectral wavelength bands.
Md. Palash Uddin, Md. Al Mamun, Md. Ali Hossain   +2 more
semanticscholar   +1 more source

1D-PCA, 2D-PCA to nD-PCA

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 ...
null Hongchuan Yu, M. Bennamoun
openaire   +1 more source

Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification

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, Md. Al Mamun, M. I. Afjal, Md. Ali Hossain   +3 more
semanticscholar   +1 more source

Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection

Comput. Networks, 2019
Handling redundant and irrelevant features in high-dimension datasets has caused a long-term challenge for network anomaly detection. Eliminating such features with spectral information not only speeds up the classification process but also helps ...
F. Salo, Ali Bou Nassif, A. Essex
semanticscholar   +1 more source