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.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Björn Stenger, Josef Kittler, Roberto Cipolla, Shu-Fai Wong, Tae-Kyun Kim
• Keywords: Between-class Scatter Matrix, Computer Vision, CVPR 2007, Incremental Lda, Incremental Learning Solution, Reduced Subspace Dimension, Total Scatter Matrix