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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2012 Vol.2(6): 860-863 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.253

Factor Analysis Using Two Stages Neural Network Architecture

Sandeep Kumar, Deepak Kumar, and Rashid Ali
Abstract—Factor Analysis is the process of finding a suitable representation of the data in terms of lesser number of variables. There are a number of application areas where factor analysis is widely used e.g. signal processing, statistics, stock marketing, forecasting, approximation, compression, security, medical sciences etc. In this paper we will show that a very effective factor analysis scheme can be developed using a feed forward neural network. Our approach has the advantage of being able to analyze very large data sets while preserving the nature of the data.

Index Terms—Factor analysis, neural network, BPN, learning, information gain.

Sandeep Kumar and Deepak Kumar are with the Tata Consultancy Services Limted, Pune India (e-mail: sandeep25789@gmail.com; deepakkumar@zhcet.ac.in).
Rashid Ali is with the Aligarh Muslim University. Uttar Pradesh,Aligarh India (e-mail: rashidaliamu@rediffmail.com).


Cite:Sandeep Kumar, Deepak Kumar, and Rashid Ali, "Factor Analysis Using Two Stages Neural Network Architecture," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 860-863, 2012.

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