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Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations
Tae-Kyun Kim; Shu-Fai Wong; Stenger, B.; Kittler, J.; Cipolla, R.;
Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on
17-22 June 2007
Page(s):1
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8
Abstract:
This paper presents a new incremental learning solution for linear discriminant analysis (LDA). We apply the concept of the sufficient spanning set approximation in each update step, i.e. for the between-class scatter matrix, the projected data matrix as well as the total scatter matrix. The algorithm yields a more general and efficient solution to incremental LDA than previous methods. It also significantly reduces the computational complexity while providing a solution which closely agrees with the batch LDA result. The proposed algorithm has a time complexity of O(Nd2) and requires O(Nd) space, where d is the reduced subspace dimension and N the data dimension. We show two applications of incremental LDA: First, the method is applied to semi-supervised learning by integrating it into an EM framework. Secondly, we apply it to the task of merging large databases which were collected during MPEG standardization for face image retrieval.
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