Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matrix computations (3rd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An eigenspace update algorithm for image analysis
Graphical Models and Image Processing
Dimensionality reduction for similarity searching in dynamic databases
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Concept decompositions for large sparse text data using clustering
Machine Learning
On the effects of dimensionality reduction on high dimensional similarity search
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ACM Computing Surveys (CSUR)
IEEE Transactions on Knowledge and Data Engineering
SIAM Journal on Matrix Analysis and Applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IMMC: incremental maximum margin criterion
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast and accurate text classification via multiple linear discriminant projections
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
On self-organizing algorithms and networks for class-separability features
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Easing the Dimensionality Curse by Stretching Metric Spaces
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Incremental Laplacian eigenmaps by preserving adjacent information between data points
Pattern Recognition Letters
Binary sparse nonnegative matrix factorization
IEEE Transactions on Circuits and Systems for Video Technology
An incremental learning algorithm of recursive Fisher linear discriminant
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A new and fast implementation for null space based linear discriminant analysis
Pattern Recognition
On-line learning of mutually orthogonal subspaces for face recognition by image sets
IEEE Transactions on Image Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On-line incremental feature weighting in evolving fuzzy classifiers
Fuzzy Sets and Systems
Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications
International Journal of Computer Vision
Convergence proof of matrix dynamics for online linear discriminant analysis
Journal of Multivariate Analysis
Least squares online linear discriminant analysis
Expert Systems with Applications: An International Journal
Incremental complete LDA for face recognition
Pattern Recognition
Incremental face recognition for large-scale social network services
Pattern Recognition
Incremental learning of complete linear discriminant analysis for face recognition
Knowledge-Based Systems
Partial-update dimensionality reduction for accumulating co-occurrence events
Pattern Recognition Letters
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Dimension reduction is a critical data preprocessing step for many database and data mining applications, such as efficient storage and retrieval of high-dimensional data. In the literature, a well-known dimension reduction algorithm is Linear Discriminant Analysis (LDA). The common aspect of previously proposed LDA-based algorithms is the use of Singular Value Decomposition (SVD). Due to the difficulty of designing an incremental solution for the eigenvalue problem on the product of scatter matrices in LDA, there has been little work on designing incremental LDA algorithms that can efficiently incorporate new data items as they become available. In this paper, we propose an LDA-based incremental dimension reduction algorithm, called IDR/QR, which applies QR Decomposition rather than SVD. Unlike other LDA-based algorithms, this algorithm does not require the whole data matrix in main memory. This is desirable for large data sets. More importantly, with the insertion of new data items, the IDR/QR algorithm can constrain the computational cost by applying efficient QR-updating techniques. Finally, we evaluate the effectiveness of the IDR/QR algorithm in terms of classification error rate on the reduced dimensional space. Our experiments on several real-world data sets reveal that the classification error rate achieved by the IDR/QR algorithm is very close to the best possible one achieved by other LDA-based algorithms. However, the IDR/QR algorithm has much less computational cost, especially when new data items are inserted dynamically.