Assessment of self-organizing map variants for clustering with application to redistribution of emotional speech patterns

Moschou, Vassiliki; Ververidis, Dimitrios; Kotropoulos, Constantine

doi:10.1016/j.neucom.2007.08.013

Cited by 16 publications

(9 citation statements)

References 27 publications

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“…In order to evaluate the classification performance of the GHPSOG, we have followed the methodology proposed by [29] to assess self-organizing models in classification, which considers several quality measures. The quality measure we have used is the classification accuracy, which is a measure of the amount of input samples correctly classified, where the higher the accuracy value, the better the accuracy.…”

Section: Classificationmentioning

confidence: 99%

“…These values of the τ parameter achieve a good tradeoff between the performance and the size of the network, allowing us at the same time to compare the effects of choosing different architecture sizes on the results. In order to evaluate the performance of the GHPSOG in clustering web documents, we have again used the classification accuracy as quality measure as proposed by [29,. To this end, each cluster has been labeled with the class that is the largest in the cluster, so the cluster represents that class.…”

Section: Web Miningmentioning

confidence: 99%

“…Since the NQC defines a convex hypervolume in the DCT domain, we might consider alternative solutions that lie in the straight line segment which connects the two clipped solutions, all of which satisfy (29) In the experiments, we have chosen 10 alternative solutions equally spaced on the line segment. In order to decide which of them is the best, we train a GHPSOG with 64-dimensional vectors q (x i ) of JPEG quantized coefficients.…”

Section: E Image Deblockingmentioning

confidence: 99%

“…This way, we are selecting the image that departs the most from the distribution of artifact-full quantized blocks q (x i ). Next we smooth the selected image by iterative steering kernel regression [37], [38], and finally we clip the DCT coefficients to enforce (29) so as to avoid excessive departure from the compressed image.…”

Section: E Image Deblockingmentioning

confidence: 99%

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Growing Hierarchical Probabilistic Self-Organizing Graphs

López‐Rubio

Palomo

2011

IEEE Trans. Neural Netw.

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Since the introduction of the growing hierarchical self-organizing map, much work has been done on self-organizing neural models with a dynamic structure. These models allow adjusting the layers of the model to the features of the input dataset. Here we propose a new self-organizing model which is based on a probabilistic mixture of multivariate Gaussian components. The learning rule is derived from the stochastic approximation framework, and a probabilistic criterion is used to control the growth of the model. Moreover, the model is able to adapt to the topology of each layer, so that a hierarchy of dynamic graphs is built. This overcomes the limitations of the self-organizing maps with a fixed topology, and gives rise to a faithful visualization method for high-dimensional data.

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Section: Classificationmentioning

confidence: 99%

Section: Web Miningmentioning

confidence: 99%

Section: E Image Deblockingmentioning

confidence: 99%

Section: E Image Deblockingmentioning

confidence: 99%

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Growing Hierarchical Probabilistic Self-Organizing Graphs

López‐Rubio

Palomo

2011

IEEE Trans. Neural Netw.

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“…SOM has been widely used in many pattern recognition fields including speech signal processing [14,15]. Data of voice is a kind of time series data, and recognition can be executed using many methods such as frequency analysis, hidden Markov models (HMM), and SOM.…”

Section: Extract Featurementioning

confidence: 99%

A Voice Instruction Learning System Using PL-T-SOM

Kuremoto

Komoto

Kobayashi

et al. 2009

2009 2nd International Congress on Image and Signal Processing

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Learning and relearning ability is important to partner robots. From this point of view, here we propose an intelligent learning system with voice instruction recognition and action learning function. Transient-SOM (T-SOM), an advanced self-organizing map proposed by our previous work for hand gesture recognition and memorization is adopted and improved to be Parameter-Less T-SOM (PL-T-SOM) with an annealing plan. Learning and additional learning experiments used instructions in multiple languages including Japanese, English, Chinese andMalaysian showed the effectiveness of our proposed system.

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Vehicle Classification in Traffic Environments Using the Growing Neural Gas

Molina‐Cabello

Luque‐Baena

López‐Rubio

et al. 2017

Advances in Computational Intelligence

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Traffic monitoring is one of the most popular applications of automated video surveillance. Classification of the vehicles into types is important in order to provide the human traffic controllers with updated information about the characteristics of the traffic flow, which facilitates their decision making process. In this work, a video surveillance system is proposed to carry out such classification. First of all, a feature extraction process is carried out to obtain the most significant features of the detected vehicles. After that, a set of Growing Neural Gas neural networks is employed to determine their types. A qualitative and quantitative assessment of the proposal is carried out on a set of benchmark traffic video sequences, with favorable results. Keywords: Foreground detection • Background modeling • Probabilistic self-organising maps • Background features

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Assessment of self-organizing map variants for clustering with application to redistribution of emotional speech patterns

Cited by 16 publications

References 27 publications

Growing Hierarchical Probabilistic Self-Organizing Graphs

Growing Hierarchical Probabilistic Self-Organizing Graphs

A Voice Instruction Learning System Using PL-T-SOM

Vehicle Classification in Traffic Environments Using the Growing Neural Gas

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