A Novel Approach to Classify High Dimensional Datasets Using Supervised Manifold Learning

Classifying high-dimensional datasets is a very challenging task. In high dimensional spaces, the performance of supervised learning methods suffer due to the huge difference between number of columns and number of rows, which degrades both accuracy and efficiency performance of classification. In this paper we propose a two phase approach to the given datasets to address the present scenario. Dimension Reduction Method Supervised Locally Linear Embedding (SLLE) is applied in the first phase to reduce dimension of the datasets and in next phase classification through K-NN and SVM is done. Experiments are carried on different high dimensional datasets and then we compared of different dimension reduction and classification methods.