Dynamic self-organizing maps with controlled growth for knowledge discovery

Alahakoon, Damminda; Halgamuge, Saman K.; Srinivasan, Bala

doi:10.1109/72.846732

Cited by 455 publications

(238 citation statements)

References 8 publications

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“…Most of the publications are devoted to a variety of applications using a variant of the basic Kohonen algorithm for a single SOM. In [1] Kohonen discussed possible variations of SOMs that include: maps of varying topology that has been studied, for example, in [8,9], and tree-structured SOMs to improve the winner search procedure, e.g. [10].…”

Section: Introductionmentioning

confidence: 99%

Multimodal FeedForward Self-organizing Maps

Paplinski

Gustafsson

2005

Computational Intelligence and Security

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Abstract. We introduce a novel system of interconnected SelfOrganizing Maps that can be used to build feedforward and recurrent networks of maps. Prime application of interconnected maps is in modelling systems that operate with multimodal data as for example in visual and auditory cortices and multimodal association areas in cortex. A detailed example of animal categorization in which the feedworward network of self-organizing maps is employed is presented. In the example we operate with 18-dimensional data projected up on the 19-dimensional hyper-sphere so that the "dot-product" learning law can be used. One potential benefit of the multimodal map is that it allows a rich structure of parallel unimodal processing with many maps involved, followed by convergence into multimodal maps. More complex stimuli can therefore be processed without a growing map size.

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Section: Introductionmentioning

confidence: 99%

Multimodal FeedForward Self-organizing Maps

Paplinski

Gustafsson

2005

Computational Intelligence and Security

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“…There are many approaches which apply these algorithms in classic neural networks (Islam et al, 2009), (Bortman and Aladjem, 2009), (Han and Qiao, 2013), (Yang and Chen, 2012). Also, there are many variations of SOM that allow a more flexible structure of the output map which can be divided into two categories: In the first type, we include growing grid (GG) , incremental GG (Blackmore and Miikkulainen, 1993), growing SOM (GSOM) (Alahakoon et al, 2000) all coming with different variants. GG is the only variant which allows growing a new node from the interior of the grid (but this is a whole row or column of nodes).…”

Section: Flexible Structure In Neuralmentioning

confidence: 99%

AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization

Spanakis¹,

Weiß

2016

Proceedings of the 8th International Conference on Agents and Artificial Intelligence

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Abstract:Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization). Neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achieved through the updating of neuron reference vectors in feature space. Despite the fact that growing variants of SOM overcome the fixed structure limitation they increase computational cost and also do not allow the removal of a neuron after its introduction. In this paper, a variant of SOM is proposed called AMSOM (Adaptive Moving Self-Organizing Map) that on the one hand creates a more flexible structure where neuron positions are dynamically altered during training and on the other hand tackles the drawback of having a predefined grid by allowing neuron addition and/or removal during training. Experiments using multiple literature datasets show that the proposed method improves training performance of SOM, leads to a better visualization of the input dataset and provides a framework for determining the optimal number and structure of neurons.

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“…The Incremental Grid Growing (Blackmore and Miikkulainen, 1993) method adds new neurons o the border of the map and an expansion factor is used to control the growth process, which is similar to the Growing SOM (Alahakoon et al, 2000) method. In the Growing Grid (Ayadi et al, 2007) method entire rows and/or columns are added to the map during training, which are based on the calculated measures for each neuron.…”

Section: Introductionmentioning

confidence: 99%

Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes

2014

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The Growing Neural Gas model is used widely in artificial neural networks.However, its application is limited in some contexts by the proliferation of nodes in dense areas of the input space. In this study, we introduce some modifications to address this problem by imposing three restrictions on the insertion of new nodes. Each restriction aims to maintain the homogeneous values of selected criteria. One criterion is related to the square error of classification and an alternative approach is proposed for avoiding additional computational costs.Three parameters are added that allow the regulation of the restriction criteria.The resulting algorithm allows models to be obtained that suit specific needs by specifying meaningful parameters.

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Dynamic self-organizing maps with controlled growth for knowledge discovery

Cited by 455 publications

References 8 publications

Multimodal FeedForward Self-organizing Maps

Multimodal FeedForward Self-organizing Maps

AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization

Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes

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