Performance Analysis of Hybrid (supervised and unsupervised) method for multiclass data set

Due to the increasing demand for multivariate data analysis from the various application the dimensionality reduction becomes an important task to represent the data in low dimensional space for the robust data representation. In this paper, multivariate data analyzed by using a new approach SVM and ICA to enhance the classification accuracy in a way that data can be present in more condensed form. Traditional methods are classified into two types namely standalone and hybrid method. Standalone method uses either supervised or unsupervised approach, whereas hybrid method uses both approaches. This paper consists of SVM (support vector machine) as supervised and ICA (Independent component analysis) as a unsupervised approach for the improvement of the classification on the basis of dimensionality reduction. SVM uses SRM (structural risk minimization) principle which is very effective over ERM (empirical risk minimization) which minimizes an upper bound on the expected risk, as opposed to ERM that minimizes the error on the training data, whereas ICA uses maximum independence maximization to improve performance. The perpendicular or right angel projection is used to avoid the redundancy and to improve the dimensionality reduction. At last step a classification algorithm is used to classify the data samples and classification accuracy is measured. Experiments are performed for various two classes as well as multiclass dataset and performance of hybrid, standalone approaches are compared.