scholarly journals Semi-supervised Feature Extraction Using Independent Factor Analysis

2011 ◽  
Author(s):  
L. Oukhellou ◽  
E. Come ◽  
P. Aknin ◽  
T. Denoeux
Keyword(s):  
Factor Analysis ◽  
Feature Extraction ◽  
Independent Factor ◽  
Supervised Feature Extraction ◽  
Independent Factor Analysis

Source separation in astrophysical maps using independent factor analysis

2003 ◽  
Vol 16 (3-4) ◽  
pp. 479-491 ◽  
Author(s):  
Ercan E. Kuruoğlu ◽  
Luigi Bedini ◽  
Maria T. Paratore ◽  
Emanuele Salerno ◽  
Anna Tonazzini
Keyword(s):  
Factor Analysis ◽  
Source Separation ◽  
Independent Factor ◽  
Independent Factor Analysis

scholarly journals Choosing the number of factors in independent factor analysis model

2005 ◽  
Vol 2 (2) ◽  
Author(s):  
Cinzia Viroli
Keyword(s):  
Factor Analysis ◽  
Latent Variables ◽  
Latent Variable ◽  
Information Criteria ◽  
Ratio Test ◽  
Independent Factor ◽  
Analysis Model ◽  
Variable Model ◽  
Factor Analysis Model ◽  
Independent Factor Analysis

Independent Factor Analysis (IFA) has recently been proposed in the signal processing literature as a way to model a set of observed variables through linear combinations of hidden independent ones plus a noise term. Despite the peculiarity of its origin the method can be framed within the latent variable model domain and some parallels with the ordinary factor analysis can be drawn. If no prior information on the latent structure is available a relevant issue concerns the correct specification of the model. In this work some methods to detect the number of significant latent variables are investigated. Moreover, since the method defines a probability density function for the latent variables by mixtures of gaussians, the correct number of mixture components must also be determined. This issue will be treated according to two main approaches. The first one amounts to carry out a likelihood ratio test. The other one is based on a penalized form of the likelihood, that leads to the so called information criteria. Some simulations and empirical results on real data sets are finally presented.

scholarly journals Noisy independent factor analysis model for density estimation and classification

2010 ◽  
Vol 4 (0) ◽  
pp. 707-736 ◽  
Author(s):  
Umberto Amato ◽  
Anestis Antoniadis ◽  
Alexander Samarov ◽  
Alexandre B. Tsybakov
Keyword(s):  
Factor Analysis ◽  
Density Estimation ◽  
Independent Factor ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Independent Factor Analysis

scholarly journals Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints

2011 ◽  
Vol 15 (3) ◽  
pp. 313-326 ◽  
Author(s):  
Etienne Côme ◽  
Latifa Oukhellou ◽  
Thierry Denœux ◽  
Patrice Aknin
Keyword(s):  
Factor Analysis ◽  
Fault Diagnosis ◽  
Independent Factor ◽  
Independent Factor Analysis

scholarly journals Partially supervised Independent Factor Analysis using soft labels elicited from multiple experts: application to railway track circuit diagnosis

2011 ◽  
Vol 16 (5) ◽  
pp. 741-754 ◽  
Author(s):  
Zohra L. Cherfi ◽  
Latifa Oukhellou ◽  
Etienne Côme ◽  
Thierry Denœux ◽  
Patrice Aknin
Keyword(s):  
Factor Analysis ◽  
Railway Track ◽  
Independent Factor ◽  
Track Circuit ◽  
Multiple Experts ◽  
Independent Factor Analysis
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