Semi-supervised topo-Bayesian ARTMAP for noisy data

This paper presents a novel adaptive resonance theory (ART)-based modular architecture for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local fuzzy ART modules. It is equipped with the distinctive features of distributed higher-order activation and match functions, using dual vigilance parameters responsible for cluster similarity and data quantization. Together, these allow DDVFA to perform unsupervised modularization, create multi-prototype clustering representations, retrieve arbitrarily-shaped clusters, and control its compactness. Another important contribution is the reduction of order-dependence, an issue that affects any agglomerative clustering method. This paper demonstrates two approaches for mitigating order-dependence: preprocessing using visual assessment of cluster tendency (VAT) or postprocessing using a novel Merge ART module. The former is suitable for batch processing, whereas the latter can be used in online learning. Experimental results in the online learning mode carried out on 30 benchmark data sets show that DDVFA cascaded with Merge ART statistically outperformed the best other ART-based systems when samples were randomly presented. Conversely, they were found to be statistically equivalent in the offline mode when samples were pre-processed using VAT. Remarkably, performance comparisons to non-ART-based clustering algorithms show that DDVFA (which learns incrementally) was also statistically equivalent to the non-incremental (offline) methods of DBSCAN, single linkage hierarchical agglomerative clustering (HAC), and offline version of k-means, while retaining the appealing properties of ART. Links to the source code and data are provided. Considering the algorithm's simplicity, online learning capability, and performance, it is an ideal choice for many agglomerative clustering applications.Representation, Topology, Visual Assessment of Cluster Tendency. methods) and top-down (divisive or splitting methods) [2]. Hierarchical ART architectures generally follow two main designs [20]: (a) a series/cascade of ART modules where the output of one ART (i.e., a prototype) is the input of the next [21][22][23][24][25][26][27][28][29][30][31] or (b) parallel ART modules sharing the same inputs and using different vigilance values [6,[32][33][34][35][36][37][38][39]. Generally, the hierarchical relationships between ART modules are defined implicitly by the input signal flow, explicitly by enforcing constraints or connections, and/or by the setting of multiple vigilance parameters to define hierarchies. Alternatively, hierarchies within the same ART can be created by designing custom ART activation functions [40,41] or by analyzing its distributed activation patterns [42]. ART-based hierarchical approaches have been successfully applied, for instance, in text mining [20,43] and robotics [30,39].Another branch of clustering includes multi-prototype-based methods. These allow multiple prototypes to represent a si...

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“…TopoART has spawned several variants for unsupervised [36][37][38], supervised [73,74], and semi-supervised [90] learning paradigms.…”

Section: Fuzzy Topoartmentioning

confidence: 99%

Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence

2020

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“…Bayesian ARTMAP variants have been developed for various tasks, such as semi-supervised learning (Nooralishahi et al, 2018;Tang & Han, 2010) and associative memory (Chin et al, 2016).…”

Section: Bayesian Artmapmentioning

confidence: 99%

A survey of adaptive resonance theory neural network models for engineering applications

2019

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This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers. 7 Code repositories 54 8 Conclusions 55 2 Compute activation function(s): Tj = fT (x, θj, α), ∀j ∈ C. 3 Perform WTA competition: J = arg max j∈Λ (Tj). 4 Compute match function(s):7 Update category J: θ new J = fL(x, θ old J , β). 8 else 9 Deactivate category J: Λ ← Λ − {J}. 10 if Λ = {∅} then 11 Go to step 3. 12 else 13 Set J = |C| + 1. 14 Create new category: C ← C ∪ {J}. 15 Initialize new category: θ new J = fN (x, λ). 16 Set output: y where the vigilance criterion is M J ≥ ρ.Learning. If the winning category satisfies the vigilance test, then resonance occurs, and the radius R J and centroid m J of the winning node are updated as follows:where ρ ∈ (0, 1] is the vigilance parameter.Learning. If the winning category J satisfies M J ≥ ρ, then resonance occurs, and it is updated as follows:J J needs to be updated after the presentation of each sample, then γ k (new) can be estimated incrementally. Particularly, when there is a resonant committed node J, if x k is a meta-information feature, thenotherwise,

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“…Therefore, how high the matching value should be set will affect the stability and plasticity of the judgment. These two values are mutually exclusive, and the best matching value is usually obtained by trial-and-error [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology

Mon

2022

Sustainability

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The unsupervised algorithm of artificial intelligence (AI), named ART (Adaptive Resonance Theory), is used to first roughly classify an image, that is, after the image is processed by the edge filtering technology, the image window is divided into 25 square areas of 5 rows and 5 columns, and then, according to the location of the edge of the image, it determines whether the robot should go straight (represented by S), turn around (represented by A), stop (T), turn left (represented by L), or turn right (represented by R). Then, after sustainable ultrasonic signal acquisition and transformation into digital signals are completed, the sustainable supervised neural network named SGAFNN (Supervised Gaussian adaptive fuzzy neural network) will perform an optimal path control that can accurately control the traveling speed and turning of the robot to avoid hitting walls or obstacles. Based on the above, this paper proposes the use of the ART operation after image processing to judge the rough direction, followed by the use of the ultrasonic signal to carry out the sustainable development of artificial intelligence and to carry out accurate speed and direction SGAFNN control to avoid obstacles. After simulation and practical evaluations, the proposed method is proved to be feasible and to exhibit good performance.

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Semi-supervised topo-Bayesian ARTMAP for noisy data

Cited by 6 publications

References 29 publications

Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence

Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence

A survey of adaptive resonance theory neural network models for engineering applications

Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology

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