Abstract
Fisher discriminant analysis (FDA) has exhibited its great power to improve the performance in classification and dimensionality reduction tasks. Objects in the real world often have more than one natural feature set and therefore they often can be described by more than one views. However, traditional FDA addresses all problems with a single view. In this paper we propose multi-view FDA (MFDA) which combines traditional FDA with multi-view learning. In order to improve the performance of MFDA for multi-class case, we further propose hierarchical MFDA which combines MFDA with hierarchical metric learning. Experiments are performed on many artificial and real-world data sets. Comparisons with the single-view FDA show the effectiveness of the proposed method.
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Chen, Q., Sun, S. (2009). Hierarchical Multi-view Fisher Discriminant Analysis. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_32
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DOI: https://doi.org/10.1007/978-3-642-10684-2_32
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