Machine Learning
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
This paper presents a radar target recognition method using kernel locally linear embedding (KLLE) and a kernel-based nonlinear representative and discriminative (KNRD) classifier. Locally linear embedding (LLE) is one of the representative manifold learning algorithms for dimensionality reduction. In this paper, LLE is extended by using kernel technique, which gives rises to the KLLE algorithm. A KNRD classifier is a combined version of a kernel-based nonlinear representor (KNR) and a kernel-based nonlinear discriminaor (KND), two classifiers recently proposed for optimal representation and discrimination, respectively. KLLE is firstly utilized to reduce data dimension and extract features from a high resolution range profile (HRRP). Then, a KNRD classifier is employed for classification. Experimental results on measured profiles from three aircrafts indicate the relatively good recognition performance of the presented method.