A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure

Masuyama, Naoki; Loo, Chu Kiong; Wermter, Stefan

doi:10.1142/s0129065718500521

Cited by 19 publications

(21 citation statements)

References 53 publications

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“…Kernel Bayesian ART (KBA) [11] can handle the problems of BA by applying Kernel Bayes' Rule (KBR) [12] for selecting the nearest neighbor node instead of the general Bayes' theorem in BA, and a correntropy [25]-based alternative similarity measurement called CIM [13]. As an extension of KBA, Topological Kernel Bayesian ART (TKBA) [14] has been proposed. In TKBA, KBR is utilized for selecting the nearest neighbor node and CIM is utilized for the node insertion criterion.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In general, the ideal topological network satisfies a Delaunay diagram. However, similar to TKBA [14], a topology construction process in TCA does not guarantee to generate the Delaunay diagram. The topology construction process consists of node deletion and edge creation/deletion as follows:…”

Section: ) Topology Constructionmentioning

confidence: 99%

“…In regard to self-organizing clustering algorithms, SOINN and TopoART suffer from instability that the quality of their clustering results is highly dependent on the presentation order of input data points. One of the state-of-the-art algorithms that can deal with the above problem is Topological Kernel Bayesian ART (TKBA) [14]. TKBA is an ART-based topological clustering algorithm which inherits the advantages of KBA.…”

Section: Introductionmentioning

confidence: 99%

“…TKBA is an ART-based topological clustering algorithm which inherits the advantages of KBA. According to [14], TKBA achieves more stable self-organizing ability and higher noise reduction ability than TopoART-and SOINN-based algorithms. The superior performance of TKBA is based on the characteristics of KBR and CIM.…”

Section: Introductionmentioning

confidence: 99%

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Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

et al. 2019

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This paper proposes a topological clustering algorithm by integrating topological structure and information theoretic learning, i.e., correntropy, into adaptive resonance theory (ART). Specifically, the proposed algorithm utilizes the correntropy induced metric (CIM) for defining a similarity measure, a node insertion criterion, and an edge creation criterion. Other types of the ART-based topological clustering algorithms have been developed, however, these algorithms have various drawbacks such as a large number of parameters, sensitivity to noisy data. Moreover, generated topological networks cannot represent the distribution of data. In contrast, the proposed algorithm realizes a stable computation and reduces the number of parameters compared to existing algorithms. Furthermore, improving the ability to express the data structure more appropriately by the topological network, a mechanism that adaptively controls the node insertion criterion is introduced to the proposed algorithm. The experimental results showed that the proposed algorithm has superior performance with respect to the self-organizing and the classification abilities compared with the state-of-the-art topological clustering algorithms. INDEX TERMS Adaptive resonance theory, correntropy, information theoretic learning, topological clustering.

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Section: Literature Reviewmentioning

confidence: 99%

Section: ) Topology Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

et al. 2019

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“…Most of all, it is easy to form convex cost functions based on the correntropy, which is very convenient for training the models [39][40][41][42]. Therefore, the correntropy has been widely used in forming robust models [43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

Mei

Liu

et al. 2019

Entropy

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In recent years, the correntropy instead of the mean squared error has been widely taken as a powerful tool for enhancing the robustness against noise and outliers by forming the local similarity measurements. However, most correntropy-based models either have too simple descriptions of the correntropy or require too many parameters to adjust in advance, which is likely to cause poor performance since the correntropy fails to reflect the probability distributions of the signals. Therefore, in this paper, a novel correntropy-based extreme learning machine (ELM) called ECC-ELM has been proposed to provide a more robust training strategy based on the newly developed multi-kernel correntropy with the parameters that are generated using cooperative evolution. To achieve an accurate description of the correntropy, the method adopts a cooperative evolution which optimizes the bandwidths by switching delayed particle swarm optimization (SDPSO) and generates the corresponding influence coefficients that minimizes the minimum integrated error (MIE) to adaptively provide the best solution. The simulated experiments and real-world applications show that cooperative evolution can achieve the optimal solution which provides an accurate description on the probability distribution of the current error in the model. Therefore, the multi-kernel correntropy that is built with the optimal solution results in more robustness against the noise and outliers when training the model, which increases the accuracy of the predictions compared with other methods.

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Optimized Bayesian adaptive resonance theory mapping model using a rational quadratic kernel and Bayesian quadratic regularization

Yang

Gou

et al. 2021

Appl Intell

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A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure

Cited by 19 publications

References 53 publications

Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

Optimized Bayesian adaptive resonance theory mapping model using a rational quadratic kernel and Bayesian quadratic regularization

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