Large scale multi-class classification based on linear optimisation

One of the most hard tasks in image classification to find a method being applicable on large scale multi-class problems where the sample size and number of the features are huge. Linear discriminant analysis as a classical method for multi-class classification, which was introduced by Fisher (1936)[4], plays an important role in the machine learning society recently. The kernelized version of this method are discussed in several papers, however they generally deal with the two class version of this approach. Bartlett recognised, in 1938 [2], there is strong relationship between the Fisher Discriminant and the Canonical Correlation Analysis and this statement is valid for the multi-class case as well. Based on this work Barker et al.(2003)[1] and Rosipal et al.(2003)[10] discuss the details about this relationship and show the appropriate kernel approach to this problem. Using Canonical Correlation for multi-class …