A neural implementation of the JADE algorithm (nJADE) using higher-order neurons

Ziegaus, Christian; Lang, Elmar

doi:10.1016/s0925-2312(03)00378-3

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Easi Jade and Natural-gradient Ica1

Motivations and Aim1

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2007

2022

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Cited by 14 publications

(2 citation statements)

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“…JADE is based on the estimation of kurtosis via cumulants. A neural implementation of JADE [113] adaptively determines the mixing matrices to be jointly diagonalized with JADE. The learning rule uses higher order neurons and generalizes Oja's PCA rule.…”

Section: Easi Jade and Natural-gradient Icamentioning

confidence: 99%

Independent Component Analysis

Du¹,

Swamy

2013

Neural Networks and Statistical Learning

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Section: Easi Jade and Natural-gradient Icamentioning

confidence: 99%

Independent Component Analysis

Du¹,

Swamy

2013

Neural Networks and Statistical Learning

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“…Independent Component Analysis may also be used in finding linear decompositions where statistical independence is maximized, helping to detect hidden components highlighting particular biological processes or functions with special relevance for the expert in genetics [22]. Independent Component Analysis exploits higher order statistics using well-known algorithms [8], like FixedPoint ICA, JADE, nJADE [39], and MLE exploiting any correlation structure in the data using algorithms based on generalized eigenvalue decompositions [35,36]. For a good review on the emerging field of Genetic Signal Processing [9] is a good recent reference.…”

Section: Motivations and Aimmentioning

confidence: 99%

Independent component analysis algorithms for microarray data analysis

Măluţan

Gómez-Vilda

Borda

2010

IDA

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Oligonucleotide Microarrays have become powerful tools in Genetic Research, as they serve as parallel scanning mechanisms to detect the presence of genes using test probes composed of controlled segments of gene code built by masking techniques. The detection of each gene depends on the multichannel differential expression of perfectly matched segments against mismatched ones. This methodology, devised to robustify the detection process posses some interesting problems under the point of view of Genomic Signal Processing, as test probes express themselves in rather different patterns, not showing proportional expression levels for most of the segment pairs, as it would be expected. These cases may be influenced by unexpected hybridization dynamics, and are worth of being studied with a double objective: gain insight into hybridization dynamics in microarrays, and to improve microarray production and processing as well. Two methods are proposed in this paper: modelling the dynamics of expression dynamics and isolating gene expressions showing unexpected behaviour to proceed in their further classification and study.

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Independent Component Analysis

Du¹,

Swamy²

2019

Neural Networks and Statistical Learning

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A neural implementation of the JADE algorithm (nJADE) using higher-order neurons

Cited by 14 publications

References 24 publications

Independent Component Analysis

Independent Component Analysis

Independent component analysis algorithms for microarray data analysis

Independent Component Analysis

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