Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
GTM: the generative topographic mapping
Neural Computation
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Design of Principal Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Model for Probabilistic Principal Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Finding representative landmarks of data on manifolds
Pattern Recognition
Using graph algebra to optimize neighborhood for isometric mapping
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Neighbourhood preserving discriminant embedding in face recognition
Journal of Visual Communication and Image Representation
Clustering-based nonlinear dimensionality reduction on manifold
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Gene expression data classification using locally linear discriminant embedding
Computers in Biology and Medicine
International Journal of Communication Networks and Distributed Systems
Ensemble-Based discriminant manifold learning for face recognition
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Neighbor line-based locally linear embedding
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Classification with the hybrid of manifold learning and gabor wavelet
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Geometrical probability covering algorithm
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Adaptive nonlinear auto-associative modeling through manifold learning
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Hi-index | 0.00 |
Manifold learning approaches such as locally linear embedding algorithm (LLE) and isometric mapping (Isomap) algorithm are aimed to discover the intrinsical low dimensional variables from high-dimensional nonlinear data While, in order to achieve effective recognition tasks based on manifold learning, many problems remain to be solved In this paper, we propose unified algorithm based on LLE and linear discriminant analysis (ULLELDA) for those remained problems First, training samples are mapped into low-dimensional embedding space and then LDA algorithm is used to project samples into discriminant space for enlarging between-class distances and decreasing within-class distance Second, the unknown samples are directly mapped into discriminant space without the computation of the corresponding one in the low-dimensional embedding space Experiments on several face databases show the advantages of the proposed algorithm.