ABSTRACT
Dimensionality reduction is an important process that is often required to understand the data in a more tractable and humanly comprehensible way. This process has been extensively studied in terms of linear methods such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Factor Analysis etc. [8]. However, it has been noticed that many high dimensional data, such as a series of related images, lie on a manifold [12] and are not scattered throughout the feature space.
