Date of Award

Spring 2016

Document Type

Thesis

Degree Name

Master of Science in Computer Science - (M.S.)

Department

Computer Science

First Advisor

Usman W. Roshan

Second Advisor

Zhi Wei

Third Advisor

Dimitri Theodoratos

Abstract

In the recent years, we have huge amounts of data which we want to classify with minimal human intervention. Only few features from the data that is available might be useful in some scenarios. In those scenarios, the dimensionality reduction methods play a major role for extracting useful features. The two parameter weighted maximum variance (2P-WMV) is a generalized dimensionality reduction method of which principal component analysis (PCA) and maximum margin criterion (MMC) are special cases.. In this paper, we have extended the 2P-WMV approach from our previous work to a semi-supervised version. The objective of this work is specially to show how two parameter version of Weighted Maximum Variance (2P-WMV) performs in Semi-Supervised environment in comparison to the supervised learning. By making use of both labeled and unlabeled data, we present our method with experimental results on several datasets using various approaches.

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