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84
Controling the Magnification Factor of SelfOrganizing Feature Maps
, 1995
"... The magnification exponents ¯ occuring in adaptive map formation algorithms like Kohonen's selforganizing feature map deviate for the information theoretically optimal value ¯ = 1 as well as from the values which optimize, e.g., the mean square distortion error (¯ = 1=3 for onedimensional maps). A ..."
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Cited by 41 (7 self)
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The magnification exponents ¯ occuring in adaptive map formation algorithms like Kohonen's selforganizing feature map deviate for the information theoretically optimal value ¯ = 1 as well as from the values which optimize, e.g., the mean square distortion error (¯ = 1=3 for onedimensional 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 ¯ ! 0. We present an extension of the selforganizing 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. 1. Introduction The representation of information in topographic maps is a common property of...
SelfOrganizing Maps on noneuclidean Spaces
 Kohonen Maps
, 1999
"... INTRODUCTION The SelfOrganizing Map, as introduced by Kohonen more than a decade ago, has stimulated an enormous body of work in a broad range of applied and theoretical fields, including pattern recognition, brain theory, biological modeling, mathematics, signal processing, data mining and many m ..."
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Cited by 32 (4 self)
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INTRODUCTION The SelfOrganizing Map, as introduced by Kohonen more than a decade ago, has stimulated an enormous body of work in a broad range of applied and theoretical fields, including pattern recognition, brain theory, biological modeling, mathematics, signal processing, data mining and many more [8]. Much of this impressive success is owed to the combination of elegant simplicity in the SOM's algorithmic formulation, together with a high ability to produce useful answers for a wide variety of applied data processing tasks and even to provide a good model of important aspects of structure formation processes in neural systems. While the applications of the SOM are extremely widespread, the majority of uses still follow the original motivation of the SOM: to create dimensionreduced "feature maps" for various uses, most prominently perhaps for the purpose of data visualization. The suitability of the SOM for this task has been analyzed in great detail and linked to earlier
A neural network based hybrid system for detection, characterization and classification of shortduration oceanic signals
 IEEE Journal of Ocean Engineering
, 1992
"... AbstractAutomated identification and classification of shortduration oceanic signals obtained from passive sonar is a complex problem because of the large variability in both temporal and spectral characteristics even in signals obtained from the same source. This paper presents the design and eva ..."
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Cited by 24 (19 self)
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AbstractAutomated identification and classification of shortduration oceanic signals obtained from passive sonar is a complex problem because of the large variability in both temporal and spectral characteristics even in signals obtained from the same source. This paper presents the design and evaluation of a comprehensive classifier system for such signals. We first highlight the importance of selecting appropriate signal descriptors or feature vectors for highquality classification of realistic shortduration oceanic signals. Waveletbased feature extractors are shown to be superior to the more commonly used autoregressive coefficients and power spectral coefficients for this purpose. A variety of static neural network classifiers are evaluated and compared favorably with traditional statistical techniques for signal classification. We concentrate on those networks that are able to time out irrelevant input features and are less susceptible to noisy inputs, and introduce two new neuralnetwork based classifiers. Methods for combining the outputs of several classifiers to yield a more accurate labeling are proposed and evaluated based on the interpretation of network outputs as approximating posterior class probabilities. These methods lead to higher classification accuracy and also provide a mechanism for recognizing deviant signals and false alarms. Performance results are given for signals in the DARPA standard data set I. KeywordsNeural networks, pattern classification, passive sonar, shortduration oceanic signals, feature extraction, evidence combination. S I.
BYY Harmony Learning, Independent State Space, and Generalized APT Financial Analyses
, 2001
"... First, the relationship between factor analysis (FA) and the wellknown arbitrage pricing theory (APT) for financial market has been discussed comparatively, with a number of tobeimproved problems listed. An overview has been made from a unified perspective on the related studies in the literature ..."
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Cited by 23 (20 self)
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First, the relationship between factor analysis (FA) and the wellknown arbitrage pricing theory (APT) for financial market has been discussed comparatively, with a number of tobeimproved problems listed. An overview has been made from a unified perspective on the related studies in the literatures of statistics, control theory, signal processing, and neural networks. Second, we introduce the fundamentals of the Bayesian Ying Yang (BYY) system and the harmony learning principle which has been systematically developed in past several years as a unified statistical framework for parameter learning, regularization and model selection, in both nontemporal and temporal stochastic environments. We further show that a specific case of the framework, called BYY independent state space (ISS) system, provides a general guide for systematically tackling various FA related learning tasks and the above tobeimproved problems for the APT analyses. Third, on various specific cases of the BYY ISS s...
A multilayer selforganizing feature map for range image segmentation
 Neurul Network 8( 1) (I 995) 6786. [ 151 T. Kohonen, SeljXIrgunitution und Associurive Memory, 2nd Edition (SpringerVerlag
, 1988
"... AbstractThis paper proposes and describes a hierarchical selforganizing neural network for range image segmentation. The multilayer selforganizing feature map (MLSOFM), which is an extension of the traditional (singlelayer) selforganizing feature map ( SOFM) is seen to alleviate the shortcoming ..."
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Cited by 23 (0 self)
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AbstractThis paper proposes and describes a hierarchical selforganizing neural network for range image segmentation. The multilayer selforganizing feature map (MLSOFM), which is an extension of the traditional (singlelayer) selforganizing feature map ( SOFM) is seen to alleviate the shortcomings of the latter in the context of range image segmentation. The problem of range image segmentation is formulated as one of vector quantization and is mapped onto the MLSOFM. The MLSOFM combines the ideas of selforganization and topographic mapping with those ofmultiscale image segmentation. Experimental results using real range images are presented. KeywordsRange image segmentation, Selforganizing feature map, Neural networks, Computer vision. 1.
Adaptive Perceptual Pattern Recognition by SelfOrganizing Neural Networks: Context, Uncertainty, Multiplicity, and Scale
 NEURAL NETWORKS
, 1995
"... A new contextsensitive neural network, called an "EXIN" (excitatory+inhibitory) network, is described. EXIN networks selforganize in complex perceptual environments, in the presence of multiple superimposed patterns, multiple scales, and uncertainty. The networks use a new inhibitory learning rule ..."
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Cited by 19 (9 self)
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A new contextsensitive neural network, called an "EXIN" (excitatory+inhibitory) network, is described. EXIN networks selforganize in complex perceptual environments, in the presence of multiple superimposed patterns, multiple scales, and uncertainty. The networks use a new inhibitory learning rule, in addition to an excitatory learning rule, to allow superposition of multiple simultaneous neural activations (multiple winners), under strictly regulated circumstances, instead of forcing winnertakeall pattern classifications. The multiple activations represent uncertainty or multiplicity in perception and pattern recognition. Perceptual scission (breaking of linkages) between independent category groupings thus arises and allows effective global contextsensitive segmentation constraint satisfaction, and exclusive credit attribution. A Weber Law neurongrowth rule lets the network learn and classify input patterns despite variations in their spatial scale. Applications of the new techn...
Neural Maps in Remote Sensing Image Analysis
 Neural Networks
, 2003
"... We study the application of SelfOrganizing Maps for the analyses of remote sensing spectral images. Advanced airborne and satellitebased imaging spectrometers produce very highdimensional spectral signatures that provide key information to many scientific inves tigations about the surface and at ..."
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Cited by 15 (12 self)
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We study the application of SelfOrganizing Maps for the analyses of remote sensing spectral images. Advanced airborne and satellitebased imaging spectrometers produce very highdimensional spectral signatures that provide key information to many scientific inves tigations about the surface and atmosphere of Earth and other planets. These new, so phisticated data demand new and advanced approaches to cluster detection, visualization, and supervised classification. In this article we concentrate on the issue of faithful topo logical mapping in order to avoid false interpretations of cluster maps created by an SaM. We describe several new extensions of the standard SaM, developed in the past few years: the Growing SelfOrganizing Map, magnification control, and Generalized Relevance Learn ing Vector Quantization, and demonstrate their effect on both lowdimensional traditional multispectral imagery and 200dimensional hyperspectral imagery.
Learning Object Behaviour Models
, 1998
"... The candidate confirms that the work submitted is his own and that appropriate credit has been The human visual system is capable of interpreting a remarkable variety of often subtle, learnt, characteristic behaviours. For instance we can determine the gender of a distant walking figure from their g ..."
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Cited by 11 (0 self)
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The candidate confirms that the work submitted is his own and that appropriate credit has been The human visual system is capable of interpreting a remarkable variety of often subtle, learnt, characteristic behaviours. For instance we can determine the gender of a distant walking figure from their gait, interpret a facial expression as that of surprise, or identify suspicious behaviour in the movements of an individual within a carpark. Machine vision systems wishing to exploit such behavioural knowledge have been limited by the inaccuracies inherent in handcrafted models and the absence of a unified framework for the perception of powerful behaviour models. The research described in this thesis attempts to address these limitations, using a statistical modelling approach to provide a framework in which detailed behavioural knowledge is acquired from the observation of long image sequences. The core of the behaviour modelling framework is an optimised sampleset representation of the probability density in a behaviour space defined by a novel temporal pattern formation strategy. This representation of behaviour is both concise and accurate and facilitates the recognition of actions or events and the assessment of behaviour typicality. The inclusion of generative capabilities
Frequency sensitive competitive learning for balanced clustering on highdimensional hyperspheres
 IEEE TRANSACTIONS ON NEURAL NETWORKS
, 2004
"... Competitive learning mechanisms for clustering in general suffer from poor performance for very high dimensional (> 1000) data because of “curse of dimensionality” effects. In applications such as document clustering, it is customary to normalize the high dimensional input vectors to unit length, a ..."
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Cited by 11 (7 self)
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Competitive learning mechanisms for clustering in general suffer from poor performance for very high dimensional (> 1000) data because of “curse of dimensionality” effects. In applications such as document clustering, it is customary to normalize the high dimensional input vectors to unit length, and it is sometimes also desirable to obtain balanced clusters, i.e., clusters of comparable sizes. The spherical kmeans (spkmeans) algorithm, which normalizes the cluster centers as well as the inputs, has been successfully used to cluster normalized text documents in 2000+ dimensional space. Unfortunately, like regularkmeans and its soft EM based version,spkmeans tends to generate extremely imbalanced clusters in high dimensional spaces when the desired number of clusters is large (tens or more). In this paper, we first show that the spkmeans algorithm can be derived from a certain maximum likelihood formulation using a mixture of von MisesFisher distributions as the generative model and in fact it can be considered as a batch mode version of (normalized) competitive learning. The proposed generative model is then adapted in a principled way to yield three frequency sensitive competitive learning variants that are applicable to static data and produced high quality and well balanced clusters for highdimensional data. Like kmeans, each iteration is linear in the number of data points and in the number of clusters for all the three algorithms. We also propose a frequency sensitive algorithm to cluster streaming 1 data. Experimental results on clustering of highdimensional text data sets are provided to show the effectiveness and applicability of the proposed techniques.