Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Automatic target recognition using high-resolution radar range-profiles
Automatic target recognition using high-resolution radar range-profiles
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Data Structures and Algorithm Analysis in C++ (3rd Edition)
Data Structures and Algorithm Analysis in C++ (3rd Edition)
Information Discriminant Analysis: Feature Extraction with an Information-Theoretic Objective
IEEE Transactions on Pattern Analysis and Machine Intelligence
A kernel optimization method based on the localized kernel Fisher criterion
Pattern Recognition
Radar HRRP statistical recognition based on hypersphere model
Signal Processing
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Large Margin Feature Weighting Method via Linear Programming
IEEE Transactions on Knowledge and Data Engineering
Information theoretic feature extraction for audio-visual speech recognition
IEEE Transactions on Signal Processing
Discrete-Time Signal Processing
Discrete-Time Signal Processing
Modeling recognizing behavior of radar high resolution range profile using multi-agent system
WSEAS Transactions on Information Science and Applications
IEEE Transactions on Signal Processing
Radar HRRP target recognition based on higher order spectra
IEEE Transactions on Signal Processing
Iterated wavelet transformation and signal discrimination for HRR radar target recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
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In radar HRRP target recognition, the quality and quantity of Discriminant Information (DI), which one is more important? Accompanied with this issue, the paper proceeds to delve into DI analysis, and accordingly, three fundamental DI extraction models are proposed, i.e., PGA, PIB and AIB. Among these models, PIB and AIB both aim to obtain Between-class DI (B-DI) from individual standpoints while PGA obtains Among-class DI (A-DI) from a general viewpoint; PGA and PIB are both used for passive recognition while AIB for active recognition. In order to externalize these models, we conduct Generalized Discriminant Analysis (GDA) into them, and two GDA variations come forth, i.e., PIB-based GDA (PIB-GDA) and AIB-based GDA (AIB-GDA). Theoretical analyses and experimental results indicate as follows. Firstly, although PGA prevails in pattern recognition, but the implementation prospect is hardly optimistic on account of the weak anti-fading ability of A-DI. Compared with PGA, PIB and AIB are both more suitable to multi-class discrimination due to the relative stability of B-DI. Secondly, in general, PIB-GDA is inferior to AIB-GDA but superior to GDA to many challenges, such as computational efficiency, target quantity, aspect and sample variation, noise disturbance, etc.