The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data

Rauber, Andreas; Merkl, Dieter; Dittenbach, Michael

doi:10.1109/tnn.2002.804221

Cited by 390 publications

(196 citation statements)

References 24 publications

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“…To this end, in this stage of our study, a fixed number of neurons is better than a selfadaptable topology. The Growing Hierarchical SOMs (GHSOMs) represent another interesting tools (Rauber et al 2002). They can increase the number of neurons and layers by means of distance measurements between neuronal weights and input data.…”

Section: Event Detectionmentioning

confidence: 99%

Bio-inspired relevant interaction modelling in cognitive crowd management

Chiappino

Morerio

Regazzoni

2014

J Ambient Intell Human Comput

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Cognitive algorithms, integrated in intelligent systems, represent an important innovation in designing interactive smart environments. More in details, Cognitive Systems have important applications in anomaly detection and management in advanced video surveillance. These algorithms mainly address the problem of modelling interactions and behaviours among the main entities in a scene. A bio-inspired structure is here proposed, which is able to encode and synthesize signals, not only for the description of single entities behaviours, but also for modelling cause-effect relationships between user actions and changes in environment configurations. Such models are stored within a memory (Autobiographical Memory) during a learning phase. Here the system operates an effective knowledge transfer from a human operator towards an automatic systems called Cognitive Surveillance Node (CSN), which is part of a complex cognitive JDL-based and bio-inspired architecture. After such a knowledge-transfer phase, learned representations can be used, at different levels, either to support human decisions, by detecting anomalous interaction models and thus compensating for human shortcomings, or, in an automatic decision scenario, to identify anomalous patterns and choose the best strategy to preserve stability of the entire system. Results are presented in a video surveillance scenario, where the CSN can observe two interacting entities consisting in a simulated crowd and a human operator.These can interact within a visual 3D simulator, where crowd behaviour is modelled by means of Social Forces. The way anomalies are detected and consequently handled is demonstrated, on synthetic and also on real video sequences, in both the user-support and automatic modes.

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Section: Event Detectionmentioning

confidence: 99%

Bio-inspired relevant interaction modelling in cognitive crowd management

Chiappino

Morerio

Regazzoni

2014

J Ambient Intell Human Comput

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“…Three major enhanced SOM-like models have been proposed, which are Growing Hierarchical Self-Organizing Map (Rauber, Merkl, and Dittenbach 2002), Growing Neural Gas (Fritzke 1995), and Dynamic Adaptive Selforganizing Hybrid Model (HUNG and Wermter 2003).…”

Section: Som Algorithmmentioning

confidence: 99%

“…The Growing Hierarchical Self-Organizing Map (GHSOM) (Rauber, Merkl, and Dittenbach 2002) is composed of individual growing self-organizing maps to form a hierarchical artificial neural network model. By providing unit-growing function in training phase, GHSOM can adjust the topology and the number of units of a map according to input data and parameters automatically.…”

Section: Growing Hierarchical Self-organizing Map (Ghsom)mentioning

confidence: 99%

Visualized cognitive knowledge map integration for P2P networks

Lin

2009

Decision Support Systems

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“…Several research papers [1], [3], [14], [19], [21] have attempted to shorten the processing time of SOM. Kohonen originally identified three speedup approaches, namely, Shortcut Winner Search, Increasing the Number of Units in SOM, and Smoothing [8].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Initialization Scheme for SOM Algorithm Based on Reference Point and Filters

Shieh

Liao

Hwang

et al. 2009

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes an efficient self-organizing map algorithm based on reference point and filters. A strategy called Reference Point SOM (RPSOM) is proposed to improve SOM execution time by means of filtering with two thresholds T 1 and T 2 . We use one threshold, T 1 , to define the search boundary parameter used to search for the Best-Matching Unit (BMU) with respect to input vectors. The other threshold, T 2 , is used as the search boundary within which the BMU finds its neighbors. The proposed algorithm reduces the time complexity from O(n 2 ) to O(n) in finding the initial neurons as compared to the algorithm proposed by Su et al. [16]. The RPSOM dramatically reduces the time complexity, especially in the computation of large data set. From the experimental results, we find that it is better to construct a good initial map and then to use the unsupervised learning to make small subsequent adjustments.

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The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data

Cited by 390 publications

References 24 publications

Bio-inspired relevant interaction modelling in cognitive crowd management

Bio-inspired relevant interaction modelling in cognitive crowd management

Visualized cognitive knowledge map integration for P2P networks

An Efficient Initialization Scheme for SOM Algorithm Based on Reference Point and Filters

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