Asymmetric neighborhood functions accelerate ordering process of self-organizing maps

This paper presents methods to improve the probability density estimation in hidden Markov models for phoneme recognition by exploiting the self-organizing map (SOM) algorithm. The advantage of using the SOM is based on the created approximative topology between the mixture densities by training the Gaussian mean vectors used as the kernel centers by the SOM algorithm. The topology allows the neighboring mixtures to respond strongly to the same inputs and so most of the nearest mixtures used to approximate the current observation probability will be found in the topological neighborhood of the winner mixture. Also the knowledge about the previous winners are used to speed up the search for the new winners. Tree-search SOMs and segmental SOM training are studied aiming at faster search and suitability for HMM training. The framework for the presented experiments includes mel-cepstrum features and phoneme-wise tied mixture density HMMs.

This paper does the qualitative analysis of the watermarking technique to provide an improved healthcare system. It addresses two main problems of brain tumor detection: one is to find the region of interest, i.e., to detect brain tumor from the medical images and second is to authenticate patient information on the medical images. The system combines a region-based segmentation using self-organizing map algorithm and secure authentication using a watermarking technique based on the Berkeley wavelet transformation (BWT) algorithm. In recent times, a significant amount of interest has been developed in encouraging radiologists or clinical experts to use the technology-based system for health care. The proposed approach combines BWT for watermarking and self-organizing map and support vector machine algorithms for region-based segmentation to improve the objectives of better treatment. The experimental result of the proposed method is evaluated for performance and quality analysis on magnetic resonance imaging (MRI) images, resulted well in terms of quality parameters and imperceptibility.

Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections.

Analyzing and representing multidimensional quantitative and qualitative data: Demographic study of the Rhône valley. The domestic consumption of the Canadian families

This study focused on demonstrating the use of a self-organizing map (SOM) algorithm to elucidate patterns among variables in simulated syngas combustion. The work was implemented in two stages: (1) modelling and simulation of syngas combustion under various feed composition and reactor temperature implemented in AspenPlusTM chemical process simulation software, and (2) pattern recognition among variables using SOM algorithm implemented in MATLAB. The varied levels of feed syngas composition and reactor temperature was randomly sampled from uniform distributions using the Morris screening technique creating four thousand eight hundred simulation conditions implemented in the process simulation which consequently produced a multivariate dataset used in the SOM analysis. Results show that cylindrical SOM topology models the dataset at lower quantization error and topographic error as compared to the rectangular SOM topology indicating suitability of the former for variables pattern elucidation for the simulated combustion. Nonetheless, the variables pattern between component planes from rectangular SOM (9 × 28 grid) and those from cylindrical SOM (9 × 28 grid) are almost similar, indicating that either rectangular or cylindrical architectures may be used for variables pattern analysis. The component planes of process variables from trained SOM are a convenient visualization of the trends across all process variables.

To deal with the problem of unit exhaustion and high computational cost of Kohonen’s Self-Organizing Map (SOM), Growing SOM was proposed in last century. Meanwhile, to avoid topological twist of the map, Aoki and Aoyagi proposed an asymmetric neighborhood function to instead of the conventional symmetric one. Furthermore, Masuda et al. proposed a heuristic method named Radius Parallel Self-Organizing Map to decide the adaptive bounds of SOM during learning process. In this paper, we propose to compose these 3 methods to construct a novel efficient SOM named RP-AG-SOM: a Growing Self-Organizing Map with asymmetric neighborhood function and Variable Radius. Additionally, RP-AG-SOM is applied to a voice instruction recognition system successfully.

Monitoring of yeast fermentation by ion mobility spectrometry measurement and data visualisation with Self-Organizing Maps

This paper presents an iterative fuzzy vector quantization approach used in codebook design and its neural net algorithm, the fuzzy self-organizing feature map (FSOFM) algorithm, which is the development of the self-organizing feature map (SOFM) algorithm and the fuzzy vector quantization (FVQ) algorithms. The FVQ algorithm allows that each training vector is assigned to multiple codewords in the early stages of the codebook design. ALthough, the FVQ algorithm reduces the dependence of the resulting codebook on the initial codebook, the codewords are calculated in batch mode. The iterative fuzzy vector quantization approach is based on a gradient decent approach, and the concept of fuzzy is introduced into it. The FSOFM algorithm considers the winning output node and its neighborhood as a fuzzy set of the input node. As a result, the feature vector of the output node in the fuzzy set of the input sample can be updated by the membership function and the training vector just completing once iteration. In this paper, the LBG, FVQ, SOFM and FSOFM algorithms are used in image compression based on vector quantization. This paper evaluates the computing efficiency of these algorithms and compares the quality of the resulting codebooks.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Autonomous underwater vehicles (AUVs) have great advantages for activities in deep sea, and expected as the attractive tool. However, AUVs have various problems which should be solved. In this paper, the Self-Organizing Map (SOM) is applied as the clustering method for the navigation system. The SOM is known as one of the effective methods to extract the principle feature from many parameters and decrease the dimension of parameters. Through the competitive learning algorithms, the obtained is tuned to express specific features of the input signals. We have been investigating the possibility of navigation system based on SOM through simulations are experiments with an AUV called Twin-Burger. The learning algorithm of usual SOM is unsupervised learning. However, supervised learning algorithms should be introduced because the relationship between distances information and desirable behavior of the robot, that is, the relationship from inputs to outputs should be acquired and learned. In this paper, a supervised learning algorithm is introduced into SOM and a method to adapt the local to its environment by learning and evaluating the trajectory of robot is proposed. In the proposed method, the map is made static and digital value as teaching data. In order to include more information of environment in the initial map, the trajectories of robot are evaluated, and the evaluation is utilized in the learning process. This method enables the to have both the effect of dynamics of robot and environmental information. The efficiency of the method is investigated through the simulations and experiments.

The magnification exponents μ occurring in adaptive map formation algorithms like Kohonen's self-organizing feature map deviate for the information theoretically optimal value μ = 1 as well as from the values that optimize, e.g., the mean square distortion error (μ = 1/3 for one-dimensional maps). At the same time, models for categorical perception such as the "perceptual magnet" effect, which are based on topographic maps, require negative magnification exponents μ < 0. We present an extension of the self-organizing feature map algorithm, which utilizes adaptive local learning step sizes to actually control the magnification properties of the map. By change of a single parameter, maps with optimal information transfer, with various minimal reconstruction errors, or with an inverted magnification can be generated. Analytic results on this new algorithm are complemented by numerical simulations.

Kohonen's self-organizing map (SOM), which performs topology-preserving transformation from a high dimensional data vector space to a low-dimensional map space, provides a powerful tool for data analysis, classification and visualization in many application fields. Despite its power, SOM can only deal with vectorized data, although many expansions have been proposed for various data-type cases. This study aims to develop a novel generalization of SOM called modular network SOM (mnSOM), which enables users to deal with general data classes in a consistent manner. mnSOM has an array structure consisting of function modules that are trainable neural networks, e.g. multi-layer perceptrons (MLPs), instead of the vector units of the conventional SOM family. In the case of MLP-modules, mnSOM learns a group of systems or functions in terms of the input-output relationships, and at the same time, mnSOM generates a feature map that shows distances between the learned systems. Thus, mnSOM with MLP modules is an SOM in function space rather than in vector space. From this point of view, the conventional SOM of Kohonen's can be regarded as a special case of mnSOM, the modules consisting of fixed-value bias units. In this paper, mnSOM with MLP modules is described along with some application examples.

Forest inventory based on airborne laser scanning produces data for small grid cells. Various segmentation methods are used to cluster the grid cells into spatially continuous larger areas that correspond to forest stands. This study examined the performance of self-organizing map (SOM) in the delineation of forest stands. The SOM is also called Kohonen map or Kohonen network, according to the developer of the method. The data consisted of stand attribute grids in 16-m resolution. Spatiality was integrated in the SOM by using the geographical coordinates as criteria in the clustering. The clusters created by the SOM were spatially distinct and formed continuous areas corresponding to traditional stands when geographical coordinates were given more weight in the clustering compared to other stand attributes such as fertility class and stand basal area. The delineation improved when the attributes of the created stands were used as the starting point of repeated SOM rounds, and removing small stands at the beginning of each round. When small within-stand variation in stand attributes was targeted, the SOM often produced irregular stands consisting of disconnected parts. These delineations could be fine-tuned by applying mode filtering to the grid of stand numbers generated by the SOM algorithm.

In Kohonen’s Self-Organizing Maps (SOM) algorithm, preserving the map structure to represent the real input patterns appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by the SOM model. This paper presents detail explanation on SOM learning algorithm and its applications. Some issues related to SOM’s architecture are also discussed, namely the formulation of training data from input samples, and the Best Matching Unit (BMU) identification for better visualization of large datasets, and improvement made to the SOM algorithm.

Availability of large full-text document collections in electronic form has created a need for tools and techniques that assist users in organizing these collections. Document clustering is one of the popular methods used for this purpose. The Self-organizing map (SOM), an unsupervised algorithm for clustering and topographic mapping, has shown promising results in this task. Most of the existing SOM techniques rely on a “bag of words” document representation. Each word in the document is considered as a separate feature, ignoring the word order. In this chapter we investigate the use of phrases rather than words as document features for document clustering. We present a phrase grammar extraction technique, and use the extracted phrases as features in two different document clustering algorithms, self-organizing map (SOM) and hierarchical self-organizing map (HSOM). We present results of clustering documents from the REUTERS corpus and show an improvement in the clustering performance evaluated using the entropy and F-measure.

Pineapple is one of the potential commodities in Indonesia. This is due to high market demand, potential suitable land in Indonesia and public awareness of fruit supply. The main factor of crop failure in pineapple plants is the delay in handling pineapple plant diseases. To identify in image processing requires class grouping. Self-Organizing Map (SOM) which divides the input pattern into several groups so that the network output is in the form of the group that is most similar to the given input. However, the SOM algorithm requires data input that characterizes an object to facilitate the identification process. So, in this study the SOM algorithm was improved through color feature extraction with parameters Red, Green, Blue, Hue, Saturation and Value, as well as texture feature extraction with parameters contrast, correlation, energy, and homogeneity in the Gray Level Co-occurrence Matrix (GLCM). Based on the results of tests carried out by the SOM algorithm with color and texture feature extraction parameters, it is able to assist in increasing the accuracy value. The results of testing the SOM model with color and texture feature extraction obtained a precision value of 93.33%, recall of 92.31% and accuracy of 92.78%.