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Factorial code

Most real world data sets consist of data vectors whose individual components are not statistically independent. In other words, knowing the value of… 
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Related topics

Related topics

12 relations
Blind signal separationFactor analysisFeature (machine learning)Image processing

Broader (1)

Signal processing

Papers overview

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2018
2018
Sparse factorial code representation using independent component analysis for face recognition
This paper presents a new face recognition method based on Independent Component Analysis (ICA), named Sparse Factorial Code… 
2018
2018
Sparse factorial code representation using independent component analysis for face recognition
This paper presents a new face recognition method based on Independent Component Analysis (ICA), named Sparse Factorial Code… 
2017
2017
FACTORIAL CODING WITH ERROR CORRECTION
Context. Factorial data coding allows combining operations of cryptographic protection, intentional alteration of data, and… 
2015
2015
Discrete independent component analysis (DICA) with belief propagation
We apply belief propagation to a Bayesian bipartite graph composed of discrete independent hidden variables and discrete visible… 
2007
2007
Information Transmission by Networks of Non Linear Neurons
Received (to be inserted Revised by Publisher) We investigate the consequences of maximizing information transfer in a simple… 
2002
2002
Optimal Extraction of Hidden Causes
This paper presents a new framework extending previous work on multiple cause mixture models. We search for an optimal neural… 
2001
2001
Independent component analysis of temporal sequences forms place cells
2000
2000
Factorial Code 표현법을 이용한 얼굴 인식
얼굴인식에서 정보 이론적 접근방법은 얼굴 영상을 작은 기저 영상의 집합으로 분해하는 것을 기초로 한다. 가장 많이 쓰이고 있는 방법은 PCA를 기반으로 하는 eigenface 방법이다. PCA를 기반으로 하는 방법은 데이터의 2… 
1997
1997
The "Independent Components" of Scenes are Edge Filters
It has previously been suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys… 
1993
1993
Information processing by a perceptron in an unsupervised learning task
We study the ability of a simple neural network (a perceptron architecture, no hidden units, binary outputs) to process… 
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