期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2015
卷号：4
期号：3
页码：10768-10771
出版社：IJECS
摘要：Data classification is one of the most challenging areas in the field of Machine Learning and Pattern Recognition applicationwhere data is represented as a point in high-dimensional space. The data can be classified using supervised learning if it is already labeled.Otherwise unsupervised learning is used. To get golden point between them, Semi supervised learning is introduced which uses both labeledand unlabeled data. Analyzing the high dimensional data is the biggest challenge that can be tackled with the help of dimensionalityreduction techniques. When Dimensionality Reduction is embedded in Semi supervised learning, it gives superior performance. The purposeof dimensionality reduction is to reduce complexity of input data without losing important details.In this paper, Semi supervised learning is studied using four different approaches. Analysis and comparative study of these techniques isillustrated with the help of three datasets. Role of dimensionality reduction is also observed in the classification of data
