nils-herrmann/clean_scirepeval huggingface dataset:
On Manifold Regularization

We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner. Some transductive graph learning algorithms and standard methods including Support Vector Machines and Regularized Least Squares can be obtained as special cases. We utilize properties of Reproducing Kernel Hilbert spaces to prove new Representer theorems that provide theoretical basis for the algorithms. As a result (in contrast to purely graph based approaches) we obtain a natural out-of-sample extension to novel examples and are thus able to handle both transductive and truly semi-supervised settings. We present experimental evidence suggesting that our semisupervised algorithms are able to use unlabeled data effectively. In the absence of labeled examples, our framework gives rise to a regularized form of spectral clustering with an out-of-sample extension.
Beyond the point cloud: from transductive to semi-supervised learning

Due to its occurrence in engineering domains and implications for natural learning, the problem of utilizing unlabeled data is attracting increasing attention in machine learning. A large body of recent literature has focussed on the transductive setting where labels of unlabeled examples are estimated by learning a function defined only over the point cloud data. In a truly semi-supervised setting however, a learning machine has access to labeled and unlabeled examples and must make predictions on data points never encountered before. In this paper, we show how to turn transductive and standard supervised learning algorithms into semi-supervised learners. We construct a family of data-dependent norms on Reproducing Kernel Hilbert Spaces (RKHS). These norms allow us to warp the structure of the RKHS to reflect the underlying geometry of the data. We derive explicit formulas for the corresponding new kernels. Our approach demonstrates state of the art performance on a variety of classification tasks.
Feature selection in MLPs and SVMs based on maximum output information

This paper presents feature selection algorithms for multilayer perceptrons (MLPs) and multiclass support vector machines (SVMs), using mutual information between class labels and classifier outputs, as an objective function. This objective function involves inexpensive computation of information measures only on discrete variables; provides immunity to prior class probabilities; and brackets the probability of error of the classifier. The maximum output information (MOI) algorithms employ this function for feature subset selection by greedy elimination and directed search. The output of the MOI algorithms is a feature subset of user-defined size and an associated trained classifier (MLP/SVM). These algorithms compare favorably with a number of other methods in terms of performance on various artificial and real-world data sets.
Manifold Regularization : A Geometric Framework for Learning from Examples

We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner. Some transductive graph learning algorithms and standard methods including Support Vector Machines and Regularized Least Squares can be obtained as special cases. We utilize properties of Reproducing Kernel Hilbert spaces to prove new Representer theorems that provide theoretical basis for the algorithms. As a result (in contrast to purely graph based approaches) we obtain a natural out-of-sample extension to novel examples and so are able to handle both transductive and truly semi-supervised settings. We present experimental evidence suggesting that our semi-supervised algorithms are able to use unlabeled data effectively. Finally we have a brief discussion of unsupervised and fully supervised learning within our general framework.
Information Theoretic Feature Crediting in Multiclass Support Vector Machines

Identifying relevant features for a classification task is an important issue in machine learning. In this paper, we present a feature crediting scheme for multiclass pattern recognition tasks, that utilizes the ability of Support Vector Machines to generalize well in high dimensional feature spaces. Support Vector learning identifies a small subset of training data relevant for the classification task. They primarily tackle the binary classification problem. This scheme uses relevant examples to identify relevant features for multi-class classification. We present, and employ for this purpose, an informationtheoretic measure of classifier performance. This measure addresses the key issue of average rate of information being delivered by the classifier. It provides immunity to sampling bias in the data and sensitivity to pattern of errors made by the classifier. Empirical results on a number of datasets suggest efficient applicability to data with a very large number of features.
A Probabilistic Upper Bound on Differential Entropy

A novel probabilistic upper bound on the entropy of an unknown one-dimensional distribution, given the support of the distribution and a sample from that distribution, is presented. No knowledge beyond the support of the unknown distribution is required. Previous distribution-free bounds on the cumulative distribution function of a random variable given a sample of that variable are used to construct the bound. A simple, fast, and intuitive algorithm for computing the entropy bound from a sample is provided.
Building a classification cascade for visual identification from one example

Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). Two special challenges characterize OID. (1) Interclass variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. (2) There may be many classes but few or just one positive "training" examples per class. Due to (1), a solution must locate possibly subtle object-specific salient features (a door handle) while avoiding distracting ones (a specular highlight). However, (2) rules out direct techniques of feature selection. We describe an online algorithm that takes one model image from a known category and builds an efficient "same" vs. "different" classification cascade by predicting the most discriminative feature set for that object. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered feature sequence unique to a specific model image, maximizing cumulative information content. Learned stopping thresholds make the classifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category, without prior knowledge about the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.
Efficient Population Registration of 3D Data

We present a population registration framework that acts on large collections or populations of data volumes. The data alignment procedure runs in a simultaneous fashion, with every member of the population approaching the central tendency of the collection at the same time. Such a mechanism eliminates the need for selecting a particular reference frame a priori, resulting in a non-biased estimate of a digital atlas. Our algorithm adopts an affine congealing framework with an information theoretic objective function and is optimized via a gradient-based stochastic approximation process embedded in a multi-resolution setting. We present experimental results on both synthetic and real images.
Sign Classification for the Visually Impaired

Our world is populated with visual information that a sighted person makes use of daily. Unfortunately, the visually impaired are deprived from such information, which limits their mobility in unconstrained environments. To help alleviate this we are developing a wearable system that is capable of detecting and recognizing signs in natural scenes. The system is composed of two main components, sign detection and recognition. The sign detector, uses a conditional maximum entropy model to find regions in an image that correspond to a sign. The sign recognizer matches the hypothesized sign regions with sign images in a database. The system decides if the most likely sign is correct or if the hypothesized sign region does not belong to a sign in the database. Our data sets encompass a wide range of variability including changes in lighting, orientation and viewing angle. In this paper, we present an overview of the system and the performance of its two main components, while paying particular attention to the recognition phase. Tested on 3,975 sign images from two different data sets, the recognition phase achieves 99.5% with 35 distinct signs and 92.8% with 65 distinct signs.
Sign Classification using Local and Meta-Features

Our world is populated with visual information that a sighted person makes use of daily. Unfortunately, the visually impaired are deprived from such information, which limits their mobility in unconstrained environments. To help alleviate this we are developing a wearable system [1, 19] that is capable of detecting and recognizing signs in natural scenes. The system is composed of two main components, sign detection and recognition. The sign detector, uses a conditional maximum entropy model to find regions in an image that correspond to a sign. The sign recognizer matches the hypothesized sign regions with sign images in a database. The system decides if the most likely sign is correct or if the hypothesized sign region does not belong to a sign in the database. Our data sets encompass a wide range of variability including changes in lighting, orientation and viewing angle. In this paper, we present an overview of the system while while paying particular attention to the recognition component. Tested on 3,975 sign images from two different data sets, the recognition phase achieves accuracies of 99.5% with 35 distinct signs and 92.8% with 65 distinct signs.
Combining Local and Global Image Features for Object Class Recognition

Object recognition is a central problem in computer vision research. Most object recognition systems have taken one of two approaches, using either global or local features exclusively. This may be in part due to the difficulty of combining a single global feature vector with a set of local features in a suitable manner. In this paper, we show that combining local and global features is beneficial in an application where rough segmentations of objects are available. We present a method for classification with local features using non-parametric density estimation. Subsequently, we present two methods for combining local and global features. The first uses a "stacking" ensemble technique, and the second uses a hierarchical classification system. Results show the superior performance of these combined methods over the component classifiers, with a reduction of over 20% in the error rate on a challenging marine science application.
Many Heads Are Better Than One: Jointly Removing Bias from Multiple MRIs Using Nonparametric Maximum Likelihood

The correction of multiplicative bias in magnetic resonance images is an important problem in medical image processing, especially as a preprocessing step for quantitative measurements and other numerical procedures. Most previous approaches have used a maximum likelihood method to increase the probability of the pixels in a single image by adaptively estimating a correction to the unknown image bias field. The pixel probabilities are defined either in terms of a pre-existing tissue model, or nonparametrically in terms of the image's own pixel values. In both cases, the specific location of a pixel in the image does not influence the probability calculation. Our approach, similar to methods of joint registration, simultaneously eliminates the bias from a set of images of the same anatomy, but from different patients. We use the statistics from the same location across different patients' images, rather than within an image, to eliminate bias fields from all of the images simultaneously. Evaluating the likelihood of a particular voxel in one patient's scan with respect to voxels in the same location in a set of other patients' scans disambiguates effects that might be due to either bias fields or anatomy. We present a variety of "two-dimensional" experimental results (working with one image from each patient) showing how our method overcomes serious problems experienced by other methods. We also present preliminary results on full three-dimensional volume correction across patients.
Faces and names in the news

We demonstrate that it is possible to automatically identify and often fix inaccuracies and resolve ambiguities in a large pool of inaccurately and ambiguously labelled face images. Rich and complex datasets tend to be inaccurately labelled. Cleaning up the labelling automatically makes it possible to perform recognition experiments on interesting real-world datasets. We show quite good face clustering is possible for such a dataset. Our dataset is 45,000 face images, obtained by applying a face finder to approximately half a million captioned news images. This dataset is more realistic than usual face recognition datasets, because it contains faces captured “in the wild” in a variety of configurations with respect to the camera, taking a variety of expressions, and under illumination of widely varying color. Each face image is associated with a set of names, automatically extracted from the associated caption. Many, but not all such sets contain the correct name. We cluster face images in appropriate discriminant coordinates. We use a clustering procedure to break ambiguities in labelling and identify incorrectly labelled faces. A merging procedure then identifies variants of names that refer to the same individual. The resulting representation can be used to label faces in news images or to organize news pictures by individuals present. An alternative view of our procedure is as a process that cleans up noisy supervised data. We demonstrate how to use entropy measures to evaluate such procedures, and compare a variety of methods for cleaning up our face data set.
Names and faces in the news

We show quite good face clustering is possible for a dataset of inaccurately and ambiguously labelled face images. Our dataset is 44,773 face images, obtained by applying a face finder to approximately half a million captioned news images. This dataset is more realistic than usual face recognition datasets, because it contains faces captured "in the wild" in a variety of configurations with respect to the camera, taking a variety of expressions, and under illumination of widely varying color. Each face image is associated with a set of names, automatically extracted from the associated caption. Many, but not all such sets contain the correct name. We cluster face images in appropriate discriminant coordinates. We use a clustering procedure to break ambiguities in labelling and identify incorrectly labelled faces. A merging procedure then identifies variants of names that refer to the same individual. The resulting representation can be used to label faces in news images or to organize news pictures by individuals present. An alternative view of our procedure is as a process that cleans up noisy supervised data. We demonstrate how to use entropy measures to evaluate such procedures.
Joint MRI Bias Removal Using Entropy Minimization Across Images

The correction of bias in magnetic resonance images is an important problem in medical image processing. Most previous approaches have used a maximum likelihood method to increase the likelihood of the pixels in a single image by adaptively estimating a correction to the unknown image bias field. The pixel likelihoods are defined either in terms of a pre-existing tissue model, or non-parametrically in terms of the image's own pixel values. In both cases, the specific location of a pixel in the image is not used to calculate the likelihoods. We suggest a new approach in which we simultaneously eliminate the bias from a set of images of the same anatomy, but from different patients. We use the statistics from the same location across different images, rather than within an image, to eliminate bias fields from all of the images simultaneously. The method builds a "multi-resolution" non-parametric tissue model conditioned on image location while eliminating the bias fields associated with the original image set. We present experiments on both synthetic and real MR data sets, and present comparisons with other methods.
Hyperspacings and the estimation of information theoretic quantities

The estimation of probability densities from data is widely used as an intermediate step in the estimation of entropy, Kullback-Leibler (KL) divergence, and mutual information, and for statistical tasks such as hypothesis testing. We propose an alternative to density estimation– partitioning a space into regions whose approximate probability mass is known–that can be used for the same purposes. We call these regions hyperspacings, a generalization of spacings in one dimension. After discussing one-dimensional spacings estimates of entropy and KLdivergence, we show how hyperspacings can be used to estimate these quantities (and mutual information) in higher dimensions. Our approach outperforms certain widely used estimators based on intermediate density estimates. Using similar ideas, we also present a new distributionfree hypothesis test for distributional equivalence that compares favorably with the Kolmogorov-Smirnov test. Using hyperspacings, it is easily extended to multiple dimensions.
Learning Hyper-Features for Visual Identification

We address the problem of identifying specific instances of a class (cars) from a set of images all belonging to that class. Although we cannot build a model for any particular instance (as we may be provided with only one "training" example of it), we can use information extracted from observing other members of the class. We pose this task as a learning problem, in which the learner is given image pairs, labeled as matching or not, and must discover which image features are most consistent for matching instances and discriminative for mismatches. We explore a patch based representation, where we model the distributions of similarity measurements defined on the patches. Finally, we describe an algorithm that selects the most salient patches based on a mutual information criterion. This algorithm performs identification well for our challenging dataset of car images, after matching only a few, well chosen patches.
ICA Using Spacings Estimates of Entropy

This paper presents a new algorithm for the independent components analysis (ICA) problem based on an efficient entropy estimator. Like many previous methods, this algorithm directly minimizes the measure of departure from independence according to the estimated Kullback-Leibler divergence between the joint distribution and the product of the marginal distributions. We pair this approach with efficient entropy estimators from the statistics literature. In particular, the entropy estimator we use is consistent and exhibits rapid convergence. The algorithm based on this estimator is simple, computationally efficient, intuitively appealing, and outperforms other well known algorithms. In addition, the estimator's relative insensitivity to outliers translates into superior performance by our ICA algorithm on outlier tests. We present favorable comparisons to the Kernel ICA, FAST-ICA, JADE, and extended Infomax algorithms in extensive simulations. We also provide public domain source code for our algorithms.
An Intrinsic Coordinate System of the Developing Human Brain

Magnetic resonance imaging of newborn infants with gestational age ranging from 24 to 40 weeks indicates majordevelopmental changes in the human brain over this time period [1]. Assessment of normal and abnormal anatomicalvariability requires a coordinate system enabling inter-subjectcomparison [2,3,4]. We present a method for identifyingan intrinsic coordinate system of the developing brain by applying a minimum entropy criterion to determine afﬁnetransformations to bring a group of subject scans into alignment.
On Model Expansion, Model Contraction, Identifiability and Prior Information: Two Illustrative Scenarios Involving Mismeasured Variables

When a candidate model for data is nonidentifiable, conventional wisdom dictates that the model must be simplified somehow, in order to gain identifiability. We explore two scenarios involving mismeasured variables where in fact model expansion, as opposed to model contraction, might be used to obtain an identifiable model. We compare the merits of model contraction and model expansion. We also investigate whether it is necessarily a good idea to alter the model for the sake of identifiability. In particular, we compare the properties of estimators obtained from identifiable models to those of estimators obtained from nonidentifiable models in tandem with crude prior information. Both asymptotic theory and simulations with MCMC-based estimators are used to draw comparisons. A technical point which arises is that the asymptotic behaviour of a posterior mean from a nonidentifiable model can be investigated using standard asymptotic theory, once the posterior mean is described in terms of the identifiable part of the model only.
Extending logistic regression to model diffuse interactions

In an observational study focussed on association between a health outcome and numerous explanatory variables, the question of interactions can be problematic. Commonly, logistic regression of the outcome on the explanatory variables might be employed. Such modelling often includes an attempt to select some pairwise product interaction terms, from amongst the many such possible pairs. For several reasons, however, this can be unsatisfying. Here we consider a different approach based on a parsimonious extension of a logistic regression model without interaction terms. This extension permits an overall synergism or antagonism in how the explanatory variables combine to associate with the outcome, without any attempt to identify specific variables which give rise to interactive behaviour. We call this diffuse interaction. We elucidate some simple properties of the diffuse interaction model, and give an example of its application to epidemiological data. We also consider asymptotic behaviour in a restricted case of the model, to gain some insight into how well this kind of interaction can be detected from data.
The utility of prior information and stratification for parameter estimation with two screening tests but no gold standard

When a gold standard screening or diagnostic test is not routinely available, it is common to apply two different imperfect tests to subjects from a study population. There is a considerable literature on estimating relevant parameters from the resultant data. In the situation that test sensitivities and specificities are unknown, several inferential strategies have been proposed. One suggestion is to use rough knowledge about the unknown test characteristics as prior information in a Bayesian analysis. Another suggestion is to obtain the statistical advantage of an identified model by splitting the population into two strata with differing disease prevalences. There is some division of opinion in the epidemiological literature on the relative merits of these two approaches. This article aims to shed light on the issue, by applying some recently developed theory on the performance of Bayesian inference in non‐identified statistical models.