The objective of comparing various dimensionality techniques is to reduce feature sets in order group attributes effectively with less computational processing time and utilization memory. reduction algorithms can decrease the dataset consisting a huge number interrelated variables, while retaining dissimilarity present as much possible. In this paper we use, Standard Deviation, Variance, Princ...

submitted for future conference: • Locally Linear Embedding of Chromatic Clusterings in Temporal and Spatial Domains Talk, MathFest, July 26 29, 2017, Chicago, Illinois

In this paper we explore the utility of nonlinear dimensionality reduction techniques in the realm of facial expression analysis. First, we test the ability of nonlinear techniques to describe the higher nonlinear nature of human facial expressions. We exploit the data-driven model of an embedding to create novel facial expressions. Finally, we composite the facial expressions back on the face.

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The ever-growing amount of data stored in digital databases raises the question of how to organize and extract useful knowledge. This paper outlines some current developments in the domains of dimensionality reduction, manifold learning, and topological learning. Several aspects are dealt with, ranging from novel algorithmic approaches to their realworld applications. The issue of quality asses...

The central idea of principal component analysis (PCA) is to reduce the dimensionality of a dataset consisting of a large number of interrelated variables, while retaining as much as possible of the variation present in the dataset. In this paper, we use PCA based algorithms in two diverse genres, qualitative spatial reasoning (QSR) to achieve lossless data reduction and health informatics to a...

LLE(Local linear embedding) is a widely used approach for dimension reduction. The neighborhood selection is an important issue for LLE. In this paper, the ε-distance approach and a slightly modified version of k-nn method are introduced. For different types of datasets, different approaches are needed in order to enjoy higher chance to obtain better representation. For some datasets with compl...