Matrix computations (3rd ed.)
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
On Updating Problems in Latent Semantic Indexing
SIAM Journal on Scientific Computing
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Incremental PCA or On-Line Visual Learning and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
A kernel view of the dimensionality reduction of manifolds
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
IDR/QR: An Incremental Dimension Reduction Algorithm via QR Decomposition
IEEE Transactions on Knowledge and Data Engineering
Face Recognition Using IPCA-ICA Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant neighborhood embedding for classification
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Incremental and robust learning of subspace representations
Image and Vision Computing
Journal of Cognitive Neuroscience
Incremental tensor analysis: Theory and applications
ACM Transactions on Knowledge Discovery from Data (TKDD)
General Averaged Divergence Analysis
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Discriminative Locality Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Component Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Multimedia
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Gene Identification: Classical and Computational Intelligence Approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Gait Components and Their Application to Gender Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An Incremental Approach to the Analysis and Transformation of Workflows Using Region Trees
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel incremental principal component analysis and its application for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental Linear Discriminant Analysis for Face Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential Karhunen-Loeve basis extraction and its application to images
IEEE Transactions on Image Processing
Pattern Recognition Letters
Feature extraction using two-dimensional neighborhood margin and variation embedding
Computer Vision and Image Understanding
Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
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Dimensionality reduction and incremental learning have recently received broad attention in many applications of data mining, pattern recognition, and information retrieval. Inspired by the concept of manifold learning, many discriminant embedding techniques have been introduced to seek low-dimensional discriminative manifold structure in the high-dimensional space for feature reduction and classification. However, such graph-embedding framework-based subspace methods usually confront two limitations: 1) since there is no available updating rule for local discriminant analysis with the additive data, it is difficult to design incremental learning algorithm and 2) the small sample size (SSS) problem usually occurs if the original data exist in very high-dimensional space. To overcome these problems, this paper devises a supervised learning method, called local discriminant subspace embedding (LDSE), to extract discriminative features. Then, the incremental-mode algorithm, incremental LDSE (ILDSE), is proposed to learn the local discriminant subspace with the newly inserted data, which applies incremental learning extension to the batch LDSE algorithm by employing the idea of singular value-decomposition (SVD) updating algorithm. Furthermore, the SSS problem is avoided in our method for the high-dimensional data and the benchmark incremental learning experiments on face recognition show that ILDSE bears much less computational cost compared with the batch algorithm.