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Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations

9 years 11 months ago
Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations
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.
Björn Stenger, Josef Kittler, Roberto Cipolla
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Björn Stenger, Josef Kittler, Roberto Cipolla, Shu-Fai Wong, Tae-Kyun Kim
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