Why to Combine Reconstructive and Discriminative Information for Incremental Subspace Learning

Proceedings of the
Computer Vision Winter Workshop 2006

Ondrej Chum and Vojtech Franc

Czech Society for Cybernetics and Informatics
(Czech Pattern Recognition Society group)

In the paper we propose a novel method for incremental visual
learning by combining reconstructive and discriminative subspace
methods. This is achieved by embedding LDA learning and
classification into the incremental PCA framework. The combined
subspace consists of a truncated PCA subspace and a few additional
basis vectors that encompass the discriminative information, which
would be lost by the discarded principal vectors. As such it contains
both sufficient reconstructive information to enable incremental
learning, and the previously extracted discriminative information to
enable efficient classification as well. We demonstrate that we are
able to efficiently update the current model with new instances of
the already learned classes as well as to introduce new classes.

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