Notice Board: Call for Paper Vol. 8 Issue 10      Submission Start Date: September 30, 2021      Acceptence Notification Start: October 12, 2021      Submission End: October 18, 2021      Final MenuScript Due: October 22, 2021      Publication Date: October 31, 2021






Volume 2 Issue 7

Author Name
Rahul Patidar, Sumit Sharma
Year Of Publication
2015
Volume and Issue
Volume 2 Issue 7
Abstract
Dimensionality Reduction (DR) methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data features of interest, such as covariance, dynamical structure, and correlation between data sets, input-output relationships, and margin between data classes. In this paper we survey about methods from this disparate literature as optimization programs over matrix manifolds. We discuss about Singular Value Decomposition (SVD), principal component analysis (PCA), Support vector machines (SVM), Locally Linear Embedding (LLE), Linear discriminate analysis (LDA), canonical correlations analysis (CCA), independent component analysis (ICA), and Partial Least Squares Regression (PLS REGRESSION). Our motivation is not to be comprehensive but to present summary of basic techniques, as well as to review select state-of-the-art methods. In this survey paper we give introduction t
PaperID
2015/02/IJMERT/07/126



Notice Board :

Call for Paper
Vol. 8 Issue 10

Submission Start Date:
September 30, 2021

Acceptence Notification Start:
October 12, 2021

Submission End:
October 18, 2021

Final MenuScript Due:
October 22, 2021

Publication Date:
October 31, 2021