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
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective and Efficient Dimensionality Reduction for Large-Scale and Streaming Data Preprocessing
IEEE Transactions on Knowledge and Data Engineering
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Image recognition on streaming data is one of the most challenging topics in Image and Video Technology and incremental dimensionality reduction algorithms play a key role in online image recognition. In this paper, we present a novel supervised dimensionality reduction algorithm—Incremental Weighted Karhunen-Loève expansion based on the Between-class scatter matrix (IWKLB) for image recognition on streaming data. In comparison with Incremental PCA, IWKLB is more effective in terms of recognition rate. In comparison with Incremental LDA, it is free of small sample size problems and can directly be applied to high-dimensional image spaces with high efficiency. Experimental results conducted on AR, one benchmark face image database, demonstrate that IWKLB is more effective than IPCA and ILDA.