Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Applied numerical linear algebra
Applied numerical linear algebra
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Hardware Implementation of a PCA Learning Network by an Asynchronous PDM Digital Circuit
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
Development of a fine-grained parallel Karhunen-Loève transform
Journal of Parallel and Distributed Computing
A Principal Component Neural Network-Based Face Recognition System and Its ASIC Implementation
VLSID '05 Proceedings of the 18th International Conference on VLSI Design held jointly with 4th International Conference on Embedded Systems Design
System-on-programmable-chip implementation for on-line face recognition
Pattern Recognition Letters
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees (3-D SPIHT)
IEEE Transactions on Circuits and Systems for Video Technology
Efficient, low-complexity image coding with a set-partitioning embedded block coder
IEEE Transactions on Circuits and Systems for Video Technology
Learning in linear neural networks: a survey
IEEE Transactions on Neural Networks
Compressive-projection principal component analysis
IEEE Transactions on Image Processing
Optical flow and principal component analysis-based motion detection in outdoor videos
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Local vs. global models for effort estimation and defect prediction
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCAbased spectral transforms have been employed successfully in conjunction with JPEG2000 for hyperspectral image compression. However, the computational cost of determining the data-dependent PCA transform is high because of its traditional eigendecomposition implementation which requires calculation of a covariance matrix across the data. Several strategies for reducing the computation burden of PCA are explored, including both spatial and spectral subsampling in the covariance calculation as well as an iterative algorithm that circumvents determination of the covariance matrix entirely. Experimental results investigate the impacts of such low-complexity PCA on JPEG2000 compression of hyperspectral images, focusing on rate-distortion performance as well as data-analysis performance at an anomaly-detection task.