nils-herrmann/clean_scirepeval huggingface dataset:
Probability Density Estimation from Optimally Condensed Data Samples

The requirement to reduce the computational cost of evaluating a point probability density estimate when employing a Parzen window estimator is a well-known problem. This paper presents the Reduced Set Density Estimator that provides a kernel-based density estimator which employs a small percentage of the available data sample and is optimal in the L/sub 2/ sense. While only requiring /spl Oscr/(N/sup 2/) optimization routines to estimate the required kernel weighting coefficients, the proposed method provides similar levels of performance accuracy and sparseness of representation as Support Vector Machine density estimation, which requires /spl Oscr/(N/sup 3/) optimization routines, and which has previously been shown to consistently outperform Gaussian Mixture Models. It is also demonstrated that the proposed density estimator consistently provides superior density estimates for similar levels of data reduction to that provided by the recently proposed Density-Based Multiscale Data Condensation algorithm and, in addition, has comparable computational scaling. The additional advantage of the proposed method is that no extra free parameters are introduced such as regularization, bin width, or condensation ratios, making this method a very simple and straightforward approach to providing a reduced set density estimator with comparable accuracy to that of the full sample Parzen density estimator.
Mercer kernel-based clustering in feature space

The article presents a method for both the unsupervised partitioning of a sample of data and the estimation of the possible number of inherent clusters which generate the data. This work exploits the notion that performing a nonlinear data transformation into some high dimensional feature space increases the probability of the linear separability of the patterns within the transformed space and therefore simplifies the associated data structure. It is shown that the eigenvectors of a kernel matrix which defines the implicit mapping provides a means to estimate the number of clusters inherent within the data and a computationally simple iterative procedure is presented for the subsequent feature space partitioning of the data.
A comparison of bacterial and human prolyl oligopeptidases: 3D-QSAR analysis of amino acid and thioxo amino acid pyrrolidides and thiazolidides as inhibitors of prolyl oligopeptidases and examination of the binding sites in the homology models of prolyl oligopeptidases

The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is the discussion appearing in Internet chat lines. In this Letter a recently introduced source separation method,termed as complexity pursuit, is applied to the problem of finding topics in dynamical text and is compared against several blind separation algorithms for the problem considered. Complexity pursuit is a generalisation of projection pursuit to time series and it is able to use both higher-order statistical measures and temporal dependency information in separating the topics. Experimental results on chat line and newsgroup data demonstrate that the minimum complexity time series indeed do cor- respond to meaningful topics inherent in the dynamical text data,and also suggest the applic- ability of the method to query-based retrieval from a temporally changing text stream. Quantitative structure-activity relationships of amino acid and thioxo amino acid pyrrolidides and thiazolidides as inhibitors of bacterial (F. meningosepticum) and human placenta prolyl oligopeptidase were investigated using CoMFA and COMSIA approaches. For the bacterial POP, the best CoMFA model obtained from 16 inhibitors is a five-component model with the following statistics: R 2 = 0.852 and RMSEcv = 0.213 for the cross-validation, and R 2 = 0.954 and RMSE = 0.119 for the fitted. The best COMSIA model obtained is a two-component model with the following statistics: R 2 = 0.826 and RMSEcv = 0.203 for the cross-validation, and R 2 = 0.915 and RMSE = 0.142 for the fitted. For the human placenta POP, the best CoMFA model obtained is again a five-component model. This model has the following statistics: R 2 = 0.885 and RMSEcv = 0.470 for the cross-validation, and R 2 = 0.966 and RMSE = 0.256 for the fitted. The best COMSIA model obtained is a two- component model with the following statistics: R 2 = 0.922 and RMSEcv = 0.339 for the cross-validation, and R 2 = 0.959 and RMSE = 0.247 for the fitted. A comparison of the homology models of the human and the bacterial POP reveals similarities and differences in the binding sites of the two proteins and provides some clues for the observed selectivity.
Orthogonal Series Density Estimation and the Kernel Eigenvalue Problem

Kernel principal component analysis has been introduced as a method of extracting a set of orthonormal nonlinear features from multivariate data, and many impressive applications are being reported within the literature. This article presents the view that the eigenvalue decomposition of a kernel matrix can also provide the discrete expansion coefficients required for a nonparametric orthogonal series density estimator. In addition to providing novel insights into nonparametric density estimation, this article provides an intuitively appealing interpretation for the nonlinear features extracted from data using kernel principal component analysis.
Advances in Information Retrieval

This book constitutes the refereed proceedings of the 24th European Colloquium on Information Retrieval Research, ECIR 2002, held in Glasgow, UK, in March 2002. The 23 revised full papers presented were carefully reviewed and selected from a total of 52 submissions. The papers are organized in topical sections on multimedia, Web-information retrieval, query modification, soft computing, models, categorization, structured documents, cross-language issues, and interactive systems.
A General Framework for a Principled Hierarchical Visualization of Multivariate Data

We present a general framework for interactive visualization and analysis of multi-dimensional data points. The proposed model is a hierarchical extension of the latent trait family of models developed in  as a generalization of GTM to noise models from the exponential family of distributions. As some members of the exponential family of distributions are suitable for modeling discrete observations, we give a brief example of using our methodology in interactive visualization and semantic discovery in a corpus of text-based documents. We also derive formulas for computing local magnification factors of latent trait projection manifolds.
Advances in Information Retrieval: 24th BCS-IRSG European Colloquium on IR Research Glasgow, UK, March 25-27, 2002 Proceedings

Evaluating a Melody Extraction Engine.- Organising and Searching Partially Indexed Image Databases.- Combining Features for Content-Based Sketch Retrieval - A Comparative Evaluation of Retrieval Performance.- The Web.- Combining Web Document Representations in a Bayesian Inference Network Model Using Link and Content-Based Evidence.- An Improved Computation of the PageRank Algorithm.- Serving Enhanced Hypermedia Information.- Query Modification.- The Use of Implicit Evidence for Relevance Feedback in Web Retrieval.- Subject Knowledge, Source of Terms, and Term Selection in Query Expansion: An Analytical Study.- Automatic Profile Reformulation Using a Local Document  A Study on Using Genetic Niching for Query Optimisation in Document Retrieval.- Concept Based Adaptive IR Model Using FCA-BAM Combination for Concept Representation and Encoding.- Models.- A Layered Bayesian Network Model for Document Retrieval.- Term Frequency Normalization via Pareto Distributions.- Optimal Mixture Models in IR.- Categorization.- Text Categorization: An Experiment Using Phrases.- A Hierarchical Model for Clustering and Categorising Documents.- Uncertainty-Based Noise Reduction and Term Selection in Text Categorization.- Structured Documents.- A Graphical User Interface for Structured Document Retrieval.- The Accessibility Dimension for Structured Document Retrieval.- Cross-Language.- Building Bilingual Dictionaries from Parallel Web Documents.- Translation-Based Indexing for Cross-Language Retrieval.- Interactive Systems.- A Retrospective Evaluation Method for Exact-Match and Best-Match Queries Applying an Interactive Query Performance Analyser.- Genre Classification and Domain Transfer for Information Filtering.
A Combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data

We present a general framework for data analysis and visualization by means of topographic organization and clustering. Imposing distributional assumptions on the assumed underlying latent factors makes the proposed model suitable for both visualization and clustering. The system noise will be modeled in parametric form, as a member of the exponential family of distributions and this allows us to deal with different (continuous or discrete) types of observables in a unified framework. In this paper, we focus on discrete case formulations which, contrary to self organizing methods for continuous data, imply variants of Bregman divergencies as measures of dissimilarity between data and reference points and, also, define the matching nonlinear relation between latent and observable variables. Therefore, the trait variant of the model can be seen as a data-driven noisy nonlinear independent component analysis, which is capable of revealing meaningful structure in the multivariate observable data and visualizing it in two dimensions. The class variant (which performs the clustering) of our model performs data-driven parametric mixture modeling. The combined (trait and class) model along with the associated estimation procedures allows us to interpret the visualization result, in the sense of a topographic ordering. One important application of this work is the discovery of underlying semantic structure in text-based documents. Experimental results on various subsets of the 20-News groups text corpus and binary coded digits data are given by way of demonstration.
An Expectation-Maximization Approach to Nonlinear Component Analysis

The proposal of considering nonlinear principal component analysis as a kernel eigenvalue problem has provided an extremely powerful method of extracting nonlinear features for a number of classification and regression applications. Whereas the utilization of Mercer kernels makes the problem of computing principal components in, possibly, infinite-dimensional feature spaces tractable, there are still the attendant numerical problems of diagonalizing large matrices. In this contribution, we propose an expectation-maximization approach for performing kernel principal component analysis and show this to be a computationally efficient method, especially when the number of data points is large.
A Variational Method for Learning Sparse and Overcomplete Representations

An expectation-maximization algorithm for learning sparse and overcomplete data representations is presented. The proposed algorithm exploits a variational approximation to a range of heavy-tailed distributions whose limit is the Laplacian. A rigorous lower bound on the sparse prior distribution is derived, which enables the analytic marginalization of a lower bound on the data likelihood. This lower bound enables the development of an expectation-maximization algorithm for learning the overcomplete basis vectors and inferring the most probable basis coefficients.
Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression

In this paper, we propose the application of the Kernel Principal Component Analysis (PCA) technique for feature selection in a high-dimensional feature space, where input variables are mapped by a Gaussian kernel. The extracted features are employed in the regression problems of chaotic Mackey–Glass time-series prediction in a noisy environment and estimating human signal detection performance from brain event-related potentials elicited by task relevant signals. We compared results obtained using either Kernel PCA or linear PCA as data preprocessing steps. On the human signal detection task, we report the superiority of Kernel PCA feature extraction over linear PCA. Similar to linear PCA, we demonstrate de-noising of the original data by the appropriate selection of various nonlinear principal components. The theoretical relation and experimental comparison of Kernel Principal Components Regression, Kernel Ridge Regression and ε-insensitive Support Vector Regression is also provided.
The topographic organization and visualization of binary data using multivariate-Bernoulli latent variable models

A nonlinear latent variable model for the topographic organization and subsequent visualization of multivariate binary data is presented. The generative topographic mapping (GTM) is a nonlinear factor analysis model for continuous data which assumes an isotropic Gaussian noise model and performs uniform sampling from a two-dimensional (2-D) latent space. Despite the, success of the GTM when applied to continuous data the development of a similar model for discrete binary data has been hindered due, in part, to the nonlinear link function inherent in the binomial distribution which yields a log-likelihood that is nonlinear in the model parameters. The paper presents an effective method for the parameter estimation of a binary latent variable model-a binary version of the GTM-by adopting a variational approximation to the binomial likelihood. This approximation thus provides a log-likelihood which is quadratic in the model parameters and so obviates the necessity of an iterative M-step in the expectation maximization (EM) algorithm. The power of this method is demonstrated on two significant application domains, handwritten digit recognition and the topographic organization of semantically similar text-based documents.
Finding Topics in Dynamical Text: Application to Chat Line Discussions

The problem of analysing dynami ally evolving textual data has re ently arisen. An example of su h data is the dis ussion appearing in Internet hat lines. In this paper a re ently introdu ed method, termed omplexity pursuit, is used to extra t the topi s of a dynami al hat line disussion. Experimental results demonstrate that meaningful topi s an be found and also suggest the appli ability of the method to query-based retrieval from a temporally hanging text stream.
A probabilistic hierarchical clustering method for organising collections of text documents

A generic probabilistic framework for the unsupervised hierarchical clustering of large-scale sparse high-dimensional data collections is proposed. The framework is based on a hierarchical probabilistic mixture methodology. Two classes of models emerge from the analysis and these have been called symmetric and asymmetric models. For text data specifically both asymmetric and symmetric models based on multinomial and binomial distributions are most appropriate. An expectation maximisation parameter estimation method is provided for all of these models. An experimental comparison of the models is obtained for two extensive online document collections.
Extraction of Sleep-Spindles from the Electroencephalogram (EEG)

Independent component analysis (ICA) is a powerful tool for separating signals from their observed mixtures. This area of research has produced many varied algorithms and approaches to the solution of this problem. The majority of these methods adopt a truly blind approach and disregard available a priori information in order to extract the original sources or a specific desired signal. In this contribution we propose a fixed point algorithm which utilizes a priori information in finding a specified signal of interest from the sensor measurements. This technique is applied to the extraction and channel isolation of sleep spindles from a multi-channel electroencephalograph (EEG).
Initialized and guided EM-clustering of sparse binary data with application to text based documents

We investigate an alternative way of combining classification and clustering techniques for sparse binary data in order to reduce the amount of training samples required. Initializing EM from the available labels also reduces the algorithms' known dependency on the initialization, which is more evident in the case of sparse data. In addition, the two-valued Poisson class-model is proposed in this paper as a sparse variant of the usual binomial assumption. Our method can be seen as a fusion between generalized logistic regression and parametric mixture modeling. Comparative simulation results on subsets of the 20 Newsgroups' binary coded text corpora and binary handwritten digits data demonstrate the potential usefulness of the suggested method.
A generative model for sparse discrete binary data with non-uniform categorical priors

The Generative T opographicMapping (GTM)was developed and introduced as a principle dalternativ eto the Self-Organising Map for, principally, visualising high dimensional continuous data. There are many cases where the observation data is ordinal and discrete and the application of methods developed speci cally for continuous data is inappropriate. Based on the continuous GTM data model a non-linear latent variable model for modeling sparse high dimensional binary data is presen ted. The primary motivation forthis w ork is the requirement for a dense and low dimensional representation of sparse binary vector space models of text documents based on the multiv ariate Bernoulli event model. The method is however applicable to binary data in general.
The organisation and visualisation of document corpora: a probabilistic approach

A generic probabilistic framework for the unsupervised organisation and visualisation of document collections is presented. The probabilistic hierarchical clustering of large-scale sparse and high-dimensional data collections is achieved by the development of a family of latent class models which are parameterized using the expectation maximisation algorithm. The framework is based on a hierarchical probabilistic mixture methodology. Two classes of models emerge from the analysis and these have been termed as symmetric and asymmetric models. For text data specifically, both asymmetric and symmetric models based on the multinomial and binomial distributions are most appropriate. The subsequent visualisation of document collections is achieved by exploiting the topographic relations between similar documents. A latent trait model is developed which provides the means of viewing vector space document representations on a 2D grid and thereby visualising the inherent structure of the document collection. A number of experiments are provided to demonstrate the technique and a concluding discussion on the proposed models is given.
Kernel PCA Feature Extraction of Event-Related Potentials for Human Signal Detection Performance

In this paper, we propose the application of the Kernel PCA technique for feature selection in high-dimensional feature space where input variables are mapped by a Gaussian kernel. The extracted features are employed in the regression problem of estimating human signal detection performance from brain event-related potentials elicited by task relevant signals. We report the superiority of Kernel PCA for feature extraction over linear PCA.
Implementing Bayesian Methods for Ion Channel Modelling

Ion channels are transmembrane proteins which are crucial for cell excitability. Ligand-gated channels open and close by undergoing conformational changes in the protein shape in response to the binding of a chemical, such as a neurotransmitter. Why is Modelling Ion Channel Structure Important? Understanding dynamical changes in ion channel structure is key to understanding channel function. Structural data gives static pictures of some of the channel’s most stable states but cannot account for dynamical changes as the channel opens and closes in the membrane. •Modelling the gating process therefore helps suggest and subsequently validate conformational changes which occur during channel gating • From this, we can explain how different agonists or mutations affect the speed or the nature of conformational changes • This will help predict the impact of novel agonists leading to a process of rational drug design
The Non-Linear PCA Algorithm and Blind Source Separation

In this chapter, Oja’s non-linear PCA algorithm is considered in detail. The signal separating properties exhibited by the algorithm are also studied. The non-linear PCA learning rule is an approximate stochastic gradient descent algorithm to minimise the mean square error incurred in representing a vector by a nonlinear projection onto a basis of reduced dimensionality. By considering the original cost function from a differing perspective we can show that the algorithm is in fact, an approximate stochastic equivalent to the batch algorithm proposed and developed by Comon.
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources

An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have sub- and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub- and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.
Fourth Order Cumulant Based Blind Source Separation

The main focus of this book is self-organising neural networks for signal separation. However, no work on blind source separation (BSS) would be complete without considering the techniques which have been developed from the classical statistical signal processing community so this chapter presents the most significant works on BSS. Chapter 4 focuses on self-organising neural networks and their emergent properties especially when considered within the bounds of the BSS problem.
Comparison of wideband LMS, subband LMS, and a nonlinear entropic neural approach to adaptive noise cancellation for speech enhancement

An empirical comparison of three adaptive algorithms for speech enhancement in noisy reverberant conditions is presented. The subband least mean square (LMS) [E. Toner and D. R. Campbell,  (1993)] and minimum entropy noise reduction schemes [M. Girolami, , 1437–1438 (1997)] are compared with a reference wideband LMS approach [S. D. Stearns and R. A. David, Signal Processing Algorithms (Prentice–Hall, Englewood Cliffs, NJ, 1988)]. All three methods were applied to the enhancement of speech corrupted with speech‐shaped noise. The schemes were tested using simulated anechoic and real‐room reverberant (T60=0.3 s) environments. The anechoic and real‐room recordings were binaural including head shadow and diffraction effects. The subband LMS adaptive processing scheme uses the LMS adaptive noise cancellation algorithm in frequency‐limited subbands. The inputs from each microphone are split into 16 contiguous subbands using a cochlear distribution according the fun...