A two stage vector quantization approach via self-organizing map

In this paper, a two-stage algorithm for vector quantization is proposed based on a self-organizing map (SOM) neural network. First, a conventional self-organizing map is modified to deal with dead codebooks in the learning process and is then used to obtain the codebook distribution structure for a given set of input data. Next, subblocks are classified based on the previous structure distribution with a prior criteria. Then, the conventional LBG algorithm is applied to these sub-blocks for data classification with initial values obtained via the SOM. Finally, extensive simulations illustrate that the proposed two-stage algorithm is very effective.

Beyond the topological map : developing alternate mappings in self organisation

The self organising map is a well established unsupervised
learning technique which is able to form sophisticated representations of an input data set. However, conventional Self Organising Map (SOM) algorithms are limited to the production of topological maps — that is, maps where distance between points on the map have a direct relationship to the Euclidean distance between the training vectors corresponding to those points.

It would be desirable to be able to create maps which form clusters on primitive attributes other than Euclidean distance; for example, clusters based upon orientation or shape. Such maps could provide a novel approach to pattern recognition tasks by providing a new method to associate groups of data.

In this paper, it is shown that the type of map produced by SOM algorithms is a direct consequence of the lateral connection strategy employed. Given this knowledge, a technique is required to establish the feasability of using an alternative lateral connection strategy. Such a technique is presented. Using this technique, it is possible to rule out lateral connection strategies that will not produce output states useful to the organisation process. This technique is demonstrated using conventional Laplacian interconnection as well as a number of novel interconnection strategies.

Electron backscatter diffraction (EBSD) map of AZ31 compressed to 1% strain at room temperature in a direction parallel to the extrusion direction

The collection contains an EBSD map of AZ31 compressed to 1% strain at room temperature in a direction parallel to the extrusion direction. The map was collected as part of an investigation into the role of twinning in the occurrence of a yield point elongation during deformation.

A hybrid Kansei engineering system using the self-organizing map neural network

Kansei Engineering (KE), a technology founded in Japan initially for product design, translates human feelings into design parameters. Although various intelligent approaches to objectively model human functions and therelationships with the product design decisions have been introduced in KE systems, many or the approaches are not able to incorporate human subjective feelings and preferenees into the decision-making process. This paper proposes a new hybrid KE system that attempts to make the machine-based decision-making process closely resembles the real-world practice. The proposed approach assimilates human perceptive and associative abililities into the decision-making process of the computer. A number of techniques based on the Self-Organizing Map (SOM) neural network are employed in the backward KE system to reveal the underlying data structures that are involved in the decision-making process. A case study on interior design is presented to evaluate the efficacy of the proposed approach. The results obtained demonstrate tbe effectiveness of the proposed approach in developing an intelligent KE system which is able to combine huiiUUI feelings and preferences into its decision making process.

Application of growing self-organizing map to distinguish between finger tapping and non tapping from brain images

Growing self-organizing map (GSOM) has been characterized as a knowledge discovery visualization application which outshines the traditional self-organizing map (SOM) due to its dynamic structure in which nodes can grow based on the input data. GSOM is utilized as a visualization tool in this paper to cluster fMRI finger tapping and non- tapping data, demonstrating the visualization capability to distinguish between tapping or non-tapping. A unique feature of GSOM is a parameter called the spread factor whose functionality is to control the spread of the GSOM map. By setting different levels of spread factor, different granularities of region of interests within tapping or non-tapping images can be visualized and analyzed. Euclidean distance based similarity calculation is used to quantify the visualized difference between tapping and non tapping images. Once the differences are identified, the spread factor is used to generate a more detailed view of those regions to provide a better visualization of the brain regions.

Fast growing self organizing map for text clustering

This paper presents an integration of a novel document vector representation technique and a novel Growing Self Organizing Process. In this new approach, documents are represented as a low dimensional vector, which is composed of the indices and weights derived from the keywords of the document.

An index based similarity calculation method is employed on this low dimensional feature space and the growing self organizing process is modified to comply with the new feature representation model.

The initial experiments show that this novel integration outperforms the state-of-the-art Self Organizing Map based techniques of text clustering in terms of its efficiency while preserving the same accuracy level.

Exploration strategies map along fast-slow metabolic and life-history continua in muroid rodents

1Personality is highly relevant to ecology and the evolution of fast–slow metabolic and life-history strategies. One of the most important personality traits is exploratory behaviour, usually measured on an animal introduced to a novel environment (e.g. open-field test).2Here, we use a unique comparative dataset on open-field exploratory behaviour of muroid rodents to test a key assumption of a recent evolutionary model, i.e. that exploration thoroughness is positively correlated to age at first reproduction (AFR). We then examine how AFR and exploratory behaviour are related to basal metabolic rate (BMR).3Inter-specific variation in exploratory behaviour was positively correlated with AFR. Both AFR and exploration behaviour were negatively correlated with BMR. These results remained significant when taking phylogeny into account.4We suggest that species occupying unproductive and unpredictable environments simultaneously benefit from high exploration, low BMR and delayed AFR because exploration increases the likelihood of finding scarce resources, whereas low BMR and delayed reproduction enhance survival during frequent resources shortages.5This study provides the first empirical evidence for a link between personality, life-history, phylogeny and energy metabolism at the inter-specific level. The superficial-thorough exploration continuum can be mapped along the fast–slow metabolic and life-history continua.

Gradient edge map features for frontal face recognition under extreme illumination changes

Our aim in this paper is to robustly match frontal faces in the presence of extreme illumination changes, using only a single training image per person and a single probe image. In the illumination conditions we consider, which include those with the dominant light source placed behind and to the side of the user, directly above and pointing downwards or indeed below and pointing upwards, this is a most challenging problem. The presence of sharp cast shadows, large poorly illuminated regions of the face, quantum and quantization noise and other nuisance effects, makes it difficult to extract a sufficiently discriminative yet robust representation. We introduce a representation which is based on image gradient directions near robust edges which correspond to characteristic facial features. Robust edges are extracted using a cascade of processing steps, each of which seeks to harness further discriminative information or normalize for a particular source of extra-personal appearance variability. The proposed representation was evaluated on the extremely difficult YaleB data set. Unlike most of the previous work we include all available illuminations, perform training using a single image per person and match these also to a single probe image. In this challenging evaluation setup, the proposed gradient edge map achieved 0.8% error rate, demonstrating a nearly perfect receiver-operator characteristic curve behaviour. This is by far the best performance achieved in this setup reported in the literature, the best performing methods previously proposed attaining error rates of approximately 6–7%.

Growing Self Organizing Map with an Imposed Binary Search Tree for Discovering Temporal Input Patterns

In this paper the Binary Search Tree Imposed Growing Self Organizing Map (BSTGSOM) is presented as an extended version of the Growing Self Organizing Map (GSOM), which has proven advantages in knowledge discovery applications. A Binary Search Tree imposed on the GSOM is mainly used to investigate the dynamic perspectives of the GSOM based on the inputs and these generated temporal patterns are stored to further analyze the behavior of the GSOM based on the input sequence. Also, the performance advantages are discussed and compared with that of the original GSOM.