Iterative Denoising

Giles, Kendall; Trosset, Michael W.; Marchette, David J.; Priebe, Carey E.

doi:10.1007/s00180-007-0090-8

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…Recently developed methods for multiscale unsupervised structure learning (11)(12)(13) can be thought of as generalizations of manifold learning techniques, in that they can learn structures more general than manifolds, such as unions of manifolds. We adopted iterative denoising tree (IDT) methodology (11,14), which offers demonstrated utility across several domains (15,16).…”

Section: Discovery Of Behavior Types Via Multiscale Unsupervised Stru...mentioning

confidence: 99%

Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning

Vogelstein

Park

Ohyama

et al. 2014

Science

Self Cite

232

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A single nervous system can generate many distinct motor patterns. Identifying which neurons and circuits control which behaviors has been a laborious piecemeal process, usually for one observer-defined behavior at a time. We present a fundamentally different approach to neuron-behavior mapping. We optogenetically activated 1054 identified neuron lines in Drosophila larvae and tracked the behavioral responses from 37,780 animals. Application of multiscale unsupervised structure learning methods to the behavioral data enabled us to identify 29 discrete, statistically distinguishable, observer-unbiased behavioral phenotypes. Mapping the neural lines to the behavior(s) they evoke provides a behavioral reference atlas for neuron subsets covering a large fraction of larval neurons. This atlas is a starting point for connectivity- and activity-mapping studies to further investigate the mechanisms by which neurons mediate diverse behaviors.

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Section: Discovery Of Behavior Types Via Multiscale Unsupervised Stru...mentioning

confidence: 99%

Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning

Vogelstein

Park

Ohyama

et al. 2014

Science

Self Cite

232

View full text Add to dashboard Cite

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“…However, when the data set is more complex, the characteristics of the data may have different relationships in different parts of the data. To solve this problem, Giles et al [19] used an iterative denoising method to discover the structure and relationship of the data set. Using the linear and nonlinear ideas, Huang and Su [20] proposed a hierarchical discriminant analysis method (HDAM), but this method was not very effective in finding the characteristics of the data.…”

Section: Introductionmentioning

confidence: 99%

Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning

Huang

2020

Discrete Dynamics in Nature and Society

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This paper presents a new hybrid discriminant analysis method, and this method combines the ideas of linearity and nonlinearity to establish a two-layer discriminant model. The first layer is a linear discriminant model, which is mainly used to determine the distinguishable samples and subsample; the second layer is a nonlinear discriminant model, which is used to determine the subsample type. Numerical experiments on real data sets show that this method performs well compared to other classification algorithms, and its stability is better than the common discriminant models.

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Iterative Denoising

Abstract: Knowledge discovery, Text mining, Classification, Clustering,

Cited by 2 publications

References 33 publications

Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning

Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning

Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning

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