Results 1  10
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25
A unified model for probabilistic principal surfaces
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... AbstractÐPrincipal curves and surfaces are nonlinear generalizations of principal components and subspaces, respectively. They can provide insightful summary of highdimensional data not typically attainable by classical linear methods. Solutions to several problems, such as proof of existence and c ..."
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Cited by 61 (6 self)
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AbstractÐPrincipal curves and surfaces are nonlinear generalizations of principal components and subspaces, respectively. They can provide insightful summary of highdimensional data not typically attainable by classical linear methods. Solutions to several problems, such as proof of existence and convergence, faced by the original principal curve formulation have been proposed in the past few years. Nevertheless, these solutions are not generally extensible to principal surfaces, the mere computation of which presents a formidable obstacle. Consequently, relatively few studies of principal surfaces are available. Recently, we proposed the probabilistic principal surface (PPS) to address a number of issues associated with current principal surface algorithms. PPS uses a manifold oriented covariance noise model, based on the generative topographical mapping (GTM), which can be viewed as a parametric formulation of Kohonen's selforganizing map. Building on the PPS, we introduce a unified covariance model that implements PPS … 0< <1†, GTM … ˆ 1†, and the manifoldaligned GTM …>1† by varying the clamping parameter. Then, we comprehensively evaluate the empirical performance (reconstruction error) of PPS, GTM, and the manifoldaligned GTM on three popular benchmark data sets. It is shown in two different comparisons that the PPS outperforms the GTM under identical parameter settings. Convergence of the PPS is found to be identical to that of the GTM and the computational overhead incurred by the PPS decreases to 40 percent or less for more complex manifolds. These results show that the generalized PPS provides a flexible and effective way of obtaining principal surfaces. Index TermsÐPrincipal curve, principal surface, probabilistic, dimensionality reduction, nonlinear manifold, generative topographic mapping. 1
Energy Functions for SelfOrganizing Maps
, 1999
"... This paper is about the last issue. After people started to realize that there is no energy function for the Kohonen learning rule (in the continuous case), many attempts have been made to change the algorithm such that an energy can be defined, without drastically changing its properties. Here we w ..."
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Cited by 54 (1 self)
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This paper is about the last issue. After people started to realize that there is no energy function for the Kohonen learning rule (in the continuous case), many attempts have been made to change the algorithm such that an energy can be defined, without drastically changing its properties. Here we will review a simple suggestion, which has been proposed 2 and generalized in several different contexts. The advantage over some other attempts is its simplicity: we only need to redefine the determination of the winning ("best matching") unit. The energy function and corresponding learning algorithm are introduced in Section 2. We give two proofs that there is indeed a proper energy function. The first one, in Section 3, is based on explicit computation of derivatives. The second one, in Section 4 follows from a limiting case of a more general (free) energy function derived in a probabilistic setting. The energy formalism allows for a direct interpretation of disordered configurations in terms of local minima, two examples of which are treated in Section 5.
Hierarchical GTM: constructing localized nonlinear projection manifolds in a principled way
, 2001
"... It has been argued that a single twodimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization syst ..."
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Cited by 29 (6 self)
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It has been argued that a single twodimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization system PhiVis [1] in several directions: (1) We allow for nonlinear projection manifolds. The basic building block is the Generative Topographic Mapping (GTM). (2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. (3) Using tools from differential geometry we derive expressions for local directional curvatures of the projection manifold.
Generative models and bayesian model comparison for shape recognition
 in: Proceedings Ninth International Workshop on Frontiers in Handwriting Recognition
, 2004
"... Recognition of handdrawn shapes is an important and widely studied problem. By adopting a generative probabilistic framework we are able to formulate a robust and flexible approach to shape recognition which allows for a wide range of shapes and which can recognize new shapes from a single exemplar ..."
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Cited by 12 (0 self)
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Recognition of handdrawn shapes is an important and widely studied problem. By adopting a generative probabilistic framework we are able to formulate a robust and flexible approach to shape recognition which allows for a wide range of shapes and which can recognize new shapes from a single exemplar. It also provides meaningful probabilistic measures of model score which can be used as part of a larger probabilistic framework for interpreting a page of ink. We also show how Bayesian model comparison allows the tradeoff between data fit and model complexity to be optimized automatically. 1.
Mathematical Aspects of Neural Networks
 European Symposium of Artificial Neural Networks 2003
, 2003
"... In this tutorial paper about mathematical aspects of neural networks, we will focus on two directions: on the one hand, we will motivate standard mathematical questions and well studied theory of classical neural models used in machine learning. On the other hand, we collect some recent theoretic ..."
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Cited by 6 (4 self)
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In this tutorial paper about mathematical aspects of neural networks, we will focus on two directions: on the one hand, we will motivate standard mathematical questions and well studied theory of classical neural models used in machine learning. On the other hand, we collect some recent theoretical results (as of beginning of 2003) in the respective areas. Thereby, we follow the dichotomy offered by the overall network structure and restrict ourselves to feedforward networks, recurrent networks, and selforganizing neural systems, respectively.
Visual Data Mining and Machine Learning
"... Abstract. Information visualization and visual data mining leverage the human visual system to provide insight and understanding of unorganized data. In order to scale to massive sets of high dimensional data, simplification methods are needed, so as to select important dimensions and objects. Some ..."
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Cited by 5 (2 self)
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Abstract. Information visualization and visual data mining leverage the human visual system to provide insight and understanding of unorganized data. In order to scale to massive sets of high dimensional data, simplification methods are needed, so as to select important dimensions and objects. Some machine learning algorithms try to solve those problems. We give in this paper an overview of information visualization and survey the links between this field and machine learning. 1
Principal Manifold Learning by Sparse Grids
, 2008
"... In this paper we deal with the construction of lowerdimensional manifolds from highdimensional data which is an important task in data mining, machine learning and statistics. Here, we consider principal manifolds as the minimum of a regularized, nonlinear empirical quantization error functional. ..."
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Cited by 4 (2 self)
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In this paper we deal with the construction of lowerdimensional manifolds from highdimensional data which is an important task in data mining, machine learning and statistics. Here, we consider principal manifolds as the minimum of a regularized, nonlinear empirical quantization error functional. For the discretization we use a sparse grid method in latent parameter space. This approach avoids, to some extent, the curse of dimension of conventional grids like in the GTM approach. The arising nonlinear problem is solved by a descent method which resembles the expectation maximization algorithm. We present our sparse grid principal manifold approach, discuss its properties and report on the results of numerical experiments for one, two and threedimensional model problems.
A Novel Construction of Connectivity Graphs for Clustering and Visualization
 WSEAS Transactions on Computers, Issue
"... Abstract: We [5, 6] have recently investigated several families of clustering algorithms. In this paper, we show how a novel similarity function can be integrated into one of our algorithms as a method of performing clustering and show that the resulting method is superior to existing methods in tha ..."
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Cited by 3 (0 self)
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Abstract: We [5, 6] have recently investigated several families of clustering algorithms. In this paper, we show how a novel similarity function can be integrated into one of our algorithms as a method of performing clustering and show that the resulting method is superior to existing methods in that it can be shown to reliably find a globally optimal clustering rather than local optima which other methods often find. We discuss some of the current difficulties with using connectivity graphs for solving clustering problems, and then we introduce a new algorithm to build the connectivity graphs. We compare this new algorithm with some famous algorithms used to build connectivity graphs. The new algorithm is shown to be superior to those in the current literature. We also extend the method to perform topology preserving mappings and show the results of such mappings on artificial and real data.
Generalised elastic nets
, 2003
"... The elastic net was introduced as a heuristic algorithm for combinatorial optimisation and has been applied, among other problems, to biological modelling. It has an energy function which trades off a fitness term against a tension term. In the original formulation of the algorithm the tension term ..."
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Cited by 2 (0 self)
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The elastic net was introduced as a heuristic algorithm for combinatorial optimisation and has been applied, among other problems, to biological modelling. It has an energy function which trades off a fitness term against a tension term. In the original formulation of the algorithm the tension term was implicitly based on a firstorder derivative. In this paper we generalise the elastic net model to an arbitrary quadratic tension term, e.g. derived from a discretised differential operator, and give an efficient learning algorithm. We refer to these as generalised elastic nets (GENs). We give a theoretical analysis of the tension term for 1D nets with periodic boundary conditions, and show that the model is sensitive to the choice of finite difference scheme that represents the discretised derivative. We illustrate some of these issues in the context of cortical map models, by relating the choice of tension term to a cortical interaction function. In particular, we prove that this interaction takes the form of a Mexican hat for the original elastic net, and of progressively more oscillatory Mexican hats for higherorder derivatives. The results apply not only to generalised elastic nets but also to other methods using discrete differential penalties, and are expected to be useful in other areas, such as data analysis, computer graphics and optimisation problems. The elastic net was first proposed as a method to obtain good solutions to the travelling salesman problem (TSP; Durbin and Willshaw, 1987) and was subsequently also found to be a very successful cortical map model
SelfOrganizing Mixture Models
 Neurocomputing
, 2005
"... We present an expectationmaximization (EM) algorithm that yields topology preserving maps of data based on probabilistic mixture models. Our approach is applicable to any mixture model for which we have a normal EM algorithm. Compared to other mixture model approaches to selforganizing maps (SOMs) ..."
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Cited by 2 (2 self)
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We present an expectationmaximization (EM) algorithm that yields topology preserving maps of data based on probabilistic mixture models. Our approach is applicable to any mixture model for which we have a normal EM algorithm. Compared to other mixture model approaches to selforganizing maps (SOMs), the function our algorithm maximizes has a clear interpretation: it sums data loglikelihood and a penalty term that enforces selforganization. Our approach allows principled handling of missing data and learning of mixtures of SOMs. We present example applications illustrating our approach for continuous, discrete, and mixed discrete and continuous data. r 2004 Elsevier B.V. All rights reserved.