Transductive optimal component analysis

10 years 2 months ago
Transductive optimal component analysis
We propose a new transductive learning algorithm for learning optimal linear representations that utilizes unlabeled data. We pose the problem of learning linear representations as an optimization one on the underlying nonlinear manifold. An additional term is used to prefer representations with large “margins” when classifying unlabeled data in the nearest classifier sense, a generalization of transductive support vector machines to learning representations. Experimental results of the proposed algorithm on face recognition data sets show the potential significant improvement for classification accuracy on test sets.
Yuhua Zhu, Yiming Wu, Xiuwen Liu, Washington Mio
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICPR
Authors Yuhua Zhu, Yiming Wu, Xiuwen Liu, Washington Mio
Comments (0)