ABSTRACT
With massive amounts of data being generated, the need for data analysis for various applications has increased. However, most of the real-world data has very high dimensionality which makes it difficult to analyze them. The concept of dimensionality reduction is to create a low dimensional representation of this data by reducing their dimensionality for data visualization and extracting the key low dimensional features. This chapter will give a brief introduction on dimensionality reduction by discussing the need for data analysis, its applications in the real world, the importance of dimensionality reduction in data analysis and visualization followed by an overview of various unsupervised learning approaches for dimensionality reduction that will be discussed in the following chapters of this book.
