Binary Factorization in Hopfield-Like Neural Autoassociator: A Promising Tool for Data Compression

Frolov, Alexander A.; Sirota, Anton; Húsek, Dušan; Muraviev, Igor P.; Combe, Ph.

doi:10.1007/978-3-7091-0646-4_12

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Cited by 3 publications

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“…In [10] and [11], neural network based binary factor analysis has been discussed using Hope-filed like neural network. In [12]and [15] formal concept analysis is used for factor analysis.…”

Section: E Related Workmentioning

confidence: 99%

Factor Analysis Using Two Stages Neural Network Architecture

Kumar¹,

Kumar²,

Ali³

2012

IJMLC

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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.

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“…In [10] and [11], neural network based binary factor analysis has been discussed using Hope-filed like neural network. In [12]and [15] formal concept analysis is used for factor analysis.…”

Section: E Related Workmentioning

confidence: 99%