nils-herrmann/clean_scirepeval huggingface dataset:
Interactive Image Segmentation With Multiple Linear Reconstructions in Windows

This paper proposes an algorithm for interactive image segmentation. The task is formulated as a problem of graph-based transductive classification. Specifically, given an image window, the color of each pixel in it will be reconstructed linearly with those of the remaining pixels in this window. The optimal reconstruction weights will be kept unchanged to linearly reconstruct their class labels. The label reconstruction errors are estimated in each window. These errors are further collected together to develop a learning model. Then, the class information about the user specified foreground and background pixels are integrated into a regularization framework. Under this framework, a globally optimal labeling is finally obtained. The computational complexity is analyzed, and an approach for speeding up the algorithm is presented. Comparative experimental results illustrate the validity of our algorithm.
Multi-view EM algorithm and its application to color image segmentation

We propose a new algorithm, the multi-view expectation and maximization algorithm (multi-view EM), to deal with real-world learning problems where there are some natural split of features. Multi-view EM does feature split in the same manner as co-training and co-EM, two successful semi-supervised learning algorithms in text learning tasks, but it considers the multi-view learning problem in the framework of the EM algorithm. The multi-view EM algorithm has impressive advantages compared with co-training and co-EM: its convergence is theoretically guaranteed; and it can deal with multiple views instead of only two views. We utilize it for color image segmentation and discuss the phenomenon that different weights for the color view and coordinate view lead to different segmentation results.
Multilevel Belief Propagation for Fast Inference on Markov Random Fields

Graph-based inference plays an important role in many mining and learning tasks. Among all the solvers for this problem, belief propagation (BP) provides a general and efficient way to derive approximate solutions. However, for large scale graphs the computational cost of BP is still demanding. In this paper, we propose a multilevel algorithm to accelerate belief propagation on Markov Random Fields (MRF). First, we coarsen the original graph to get a smaller one. Then, BP is applied on the new graph to get a coarse result. Finally the coarse solution is efficiently refined back to derive the original solution. Unlike traditional multi- resolution approaches, our method features adaptive coarsening and efficient refinement. The above process can be recursively applied to reduce the computational cost remarkably. We theoretically justify the feasibility of our method on Gaussian MRFs, and empirically show that it is also effectual on discrete MRFs. The effectiveness of our method is verified in experiments on various inference tasks.
Sparse Norm-Regularized Reconstructive Coefficients Learning

Inspired by the fact that the final decision rule is mainly affected by a small subset of the training samples, i.e., Support Vector Machine(SVM) shows that the decision function relies on the few samples that are on or over the margin. We propose a new framework that explicitly strengthen this intuitive fact by adding an $l_1$-norm regularizer. We give different formulations for our framework in different scenarios, and the experiments show that our framework can not only lead to high sparse solutions but also better performance than traditional methods.
Human eyes detection using hybrid neural method

In this paper, a novel hybrid neural method for human eyes detection is proposed. Firstly, the input facial image is preprocessed for normalization. Then, the candidates of eyes regions are detected based on a modified version of radial basis function (RBF) neural networks. Finally, a hierarchical knowledge-based approach is presented to validate these candidates and to locate the positions of both eyes. Our method has been tested on a database of facial images and achieves a good performance.
A weighted subspace approach for improving bagging performance

Bagging is an ensemble method that uses random resampling of a dataset to construct models. In classification scenarios, the random resampling procedure in bagging induces some classification margin over the dataset. In addition, when perform bagging in different feature subspaces, the resulting classification margins are likely to be diverse. We take into account the diversity of classification margins in feature sub- spaces for improving the performance of bagging. We first study the average error rate of bagging, convert our task into an optimization problem for determining some weights for feature subspaces, and then assign the weights to the sub- spaces via a randomized technique in classifier construction. Experimental results demonstrate that our method is able to further improve the classification accuracy of bagging, and also outperforms several other ensemble methods including AdaBoost, random forests and random subspace method.
Rare Chinese character recognition by Radical Extraction Network

Building a modern Optical Character Recognition (OCR) system for Chinese is hard due to the large Chinese vocabulary list. Training images for rare Chinese characters are extremely expensive to obtain. Radical-based OCR systems tackle this problem by first extracting and recognizing basic graphical components (i.e., radicals) of a Chinese character. However, how to reliably recognize radicals still remains an open challenge. In this paper, we propose a novel Radical Extraction Network (REN) to extract and recognize radicals using deep Convolutional Neural Network (CNN). REN is end-to-end trainable, and it needs less hand-tunning compared with previous segmentation-based approaches. Deep appearance models for radicals are learned from data in a weakly supervised fashion, and no radical-level annotations are required. We learn to recognize different radicals on commonly used Chinese characters, and transfer the learned deep appearance models to rarely used Chinese characters. Experimental results show that the proposed method helps the classifier to recognize rare Chinese characters.
Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization

This work presents a weakly supervised framework with deep neural networks for vision-based continuous sign language recognition, where the ordered gloss labels but no exact temporal locations are available with the video of sign sentence, and the amount of labeled sentences for training is limited. Our approach addresses the mapping of video segments to glosses by introducing recurrent convolutional neural network for spatio-temporal feature extraction and sequence learning. We design a three-stage optimization process for our architecture. First, we develop an end-to-end sequence learning scheme and employ connectionist temporal classification (CTC) as the objective function for alignment proposal. Second, we take the alignment proposal as stronger supervision to tune our feature extractor. Finally, we optimize the sequence learning model with the improved feature representations, and design a weakly supervised detection network for regularization. We apply the proposed approach to a real-world continuous sign language recognition benchmark, and our method, with no extra supervision, achieves results comparable to the state-of-the-art.
On the Generalization Ability of Online Gradient Descent Algorithm Under the Quadratic Growth Condition

Online learning has been successfully applied in various machine learning problems. Conventional analysis of online learning achieves a sharp generalization bound with a strongly convex assumption. In this paper, we study the generalization ability of the classic online gradient descent algorithm under the quadratic growth condition (QGC), a strictly weaker condition than strong convexity. Under some mild assumptions, we prove that the excess risk converges no worse than <inline-formula> <tex-math notation="LaTeX">$O(\log T/T)$ </tex-math></inline-formula> when the data are independently and identically distributed (i.i.d.). When the data are generated from a <inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>-mixing process, we achieve the excess risk bound <inline-formula> <tex-math notation="LaTeX">$O(\log T /T+\phi (\tau))$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\phi (\tau)$ </tex-math></inline-formula> is the mixing coefficient capturing the non-i.i.d. attribute. Our key technique is based on the combination of the QGC and the martingale concentrations. Our results indicate that the strong convexity is not necessary to achieve the sharp <inline-formula> <tex-math notation="LaTeX">$O(\log {T}/T)$ </tex-math></inline-formula> convergence rate in online learning. We verify our theories on both synthetic and real-world data.
Learning an Orthogonal and Smooth Subspace for Image Classification

The recent years have witnessed a surge of interests of learning a subspace for image classification, which has aroused considerable researches from the pattern recognition and signal processing fields. However, for image classification, the accuracies of previous methods are not so high since they neglect some particular characters of the image data. In this paper, we propose a new subspace learning method. It constrains that the transformation basis is orthonormal and the derived coefficients are spatially smooth. Classification is then performed in the image subspace. The proposed method can not only represent the intrinsic structure of the image data, but also avoid over-fitting. More importantly, it can be considered as a general framework, within which the performances of other subspace learning methods can be improved in the same way. Some related analyses of the proposed approach are presented. Promising experimental results on different kinds of real images demonstrate the effectiveness of our algorithm for image classification.
Efficient multi-label classification with hypergraph regularization

Many computer vision applications, such as image classification and video indexing, are usually multi-label classification problems in which an instance can be assigned to more than one category. In this paper, we present a novel multi-label classification approach with hypergraph regularization that addresses the correlations among different categories. First, a hypergraph is constructed to capture the correlations among different categories, in which each vertex represents one training instance and each hyperedge for one category contains all the instances belonging to the same category. Then, an improved SVM like learning system incorporating the hypergraph regularization, called Rank-HLapSVM, is proposed to handle the multi-label classification problems. We find that the corresponding optimization problem can be efficiently solved by the dual coordinate descent method. Many promising experimental results on the real datasets including ImageCLEF and MediaMill demonstrate the effectiveness and efficiency of the proposed algorithm.
A semi-supervised classification method based on transduction of labeled data

The semi-supervised classification problem with partially labeled data is very important in the research area of pattern recognition and machine learning. In this paper, an approach based on transduction of labeled data is proposed to improve current classification methods. The general knowledge about the attribute of data distribution is used to carry out transduction. Employing this kind of knowledge, the commonly existent mode of the distribution corresponding to each labeled sample can be effectively found by mean shift, and the data at the mode can be regarded as having the same label with the original labeled sample with high confidence. Using the mode data instead of the original labeled data for classification can be capable of improving classification performance. Encouraging experimental results both on synthetic data and real-world handwritten characters validate the applicability and effectiveness of the approach
End-to-end Chinese text recognition

In this paper, we propose a new method for Chinese text recognition, which comprises two main contributions: First, we create a large Chinese text dataset, including 260 thousand images collected from business card and 390 thousand synthetic images generated by rendering engine. Second, we use these images to train a deep network to perform text recognition, which can recognize more than six thousand kinds of Chinese character accurately. Although our system is composed of different types of neural networks (CNN and LSTM), it is end-to-end trainable. Experiments demonstrate that our system achieve a high recognition accuracy in which synthetic data plays an important role.
Generalized RQ Minimization With Applications in Array Transmit Beamforming

This letter deals with generalized Rayleigh quotient (RQ) minimization with possibly singular matrices in the quadratic forms. An extended theorem providing closed-form analytical solutions for different cases is derived. It is shown that the theorem is an extension of the typical RQ minimization. Next, its applications in array transmit beamforming under the criterion of minimum integrated sidelobe level are illustrated. Finally, theoretical analyses and numerical comparisons are provided to validate both the theorem and the beamformers.
Compressed Spectral Clustering

Compressed sensing has received much attention in both data mining and signal processing communities. In this paper, we provide theoretical results to show that compressed spectral clustering, separating data samples into different clusters directly in the compressed measurement domain, is possible. Specifically, we provide theoretical bounds guaranteeing that if the data is measured directly in the compressed domain, spectral clustering on the compressed data works almost as well as that in the data domain. Moreover, we show that for a family of well-known compressed sensing matrices, compressed spectral clustering is universal, i. e., clustering in the measurement domain works provided that the data are sparse in some, even unknown, basis. Finally, experimental results on both toy and real world data sets demonstrate that compressed spectral clustering achieves comparable clustering performance with traditional spectral clustering that works directly in the data domain, with much less computational time.
Block-Quantized Support Vector Ordinal Regression

Support vector ordinal regression (SVOR) is a recently proposed ordinal regression (OR) algorithm. Despite its theoretical and empirical success, the method has one major bottleneck, which is the high computational complexity. In this brief, we propose a both practical and theoretical guaranteed algorithm, block-quantized support vector ordinal regression (BQSVOR), where we approximate the kernel matrix K with [(K)\tilde] that is composed of k 2 constant blocks. We provide detailed theoretical justification on the approximation accuracy of BQSVOR. Moreover, we prove theoretically that the OR problem with the block-quantized kernel matrix [(K)\tilde] could be solved by first separating the data samples in the training set into k clusters with kernel k-means and then performing SVOR on the k cluster representatives. Hence, the algorithm leads to an optimization problem that scales only with the number of clusters, instead of the data set size. Finally, experiments on several real-world data sets support the previous analysis and demonstrate that BQSVOR improves the speed of SVOR significantly with guaranteed accuracy.
Learning similarity metric with SVM

In this paper, we show how to learn a good similarity metric for SVM classification. We present a novel approach to simultaneously learn a Mahalanobis similarity metric and an SVM classifier. Different from previous approaches, we optimize the Mahalanobis metric directly for minimizing the SVM classification error. Our formulation generalizes the traditional large margin principle used in standard SVM, that is, we maximize the margin-radius-ratio. The learned similarity metric significantly improves the classification performance of standard SVM. Empirical studies on real datasets show the proposed approach achieves higher or comparable classification accuracies compared with state-of-the-art similarity learning methods.
Aspect Summarization from Blogsphere for Social Study

In this paper, we study the problem of summarizing reasons from blogsphere for social study. We regard weblogs as a source for collecting non-discrete public opinions, where genuine reasons/aspects can be found. To extract the reason inside the blogs, we define four tasks: irrelevant blog filtering, reason/non-reason classification, polarity identification, and reason summarization. We solve the reason/non-reason classification problem by selecting a set of topic related words and brief the reasons by clustering paragraphs containing aspects after sentiment classification. Initial experiments on two topics show an encouraging result on the proposed framework.  weblogs, social study, opinion extraction, reason summarization, text mining, sentiment classicization
Switching ARIMA model based forecasting for traffic flow

Switching dynamic linear models are commonly used methods to describe change in an evolving time series, where the switching ARIMA (autoregressive integrated moving average) model is a special case. Short-term forecasting of traffic flows is an essential part of intelligent traffic systems (ITS). We apply the switching ARIMA model to a traffic flow series. We have observed that the conventional switching model is inappropriate to describe the pattern changing. Thus, the variable of duration is introduced and we use the sigmoid function to describe the influence of duration to the transition probability of the patterns. Based on the switching ARIMA model, a forecasting algorithm is presented. We apply the proposed model to real data obtained from UTC/SCOOT systems in Beijing's traffic management bureau. The experiments show that our proposed model is applicable and effective.
Local Regularized Least-Square Dimensionality Reduction

In this paper, we propose a new nonlinear dimensionality reduction algorithm by adopting regularized least-square criterion on local areas of the data distribution. We first propose a local linear model to describe the characteristic of the low-dimensional coordinates of the neighborhood centered in each data point, and use regularized least-square criterion to evaluate the fitness of the low-dimensional embedding. Next, we form an optimization task similar to the graph Laplacian and efficiently retrieve the solution via eigenvalue decomposition. The relationship between our method and the Laplacian Eigenmaps are discussed, and experimental results are presented.
Homotopy Regularization for Boosting

In this paper, we present a homotopy regularization algorithm for boosting. We introduce a regularization term with adaptive weight into the boosting framework and compose a homotopy objective function. Optimization of this objective approximately composes a solution path for the regularized boosting. Following this path, we can find suitable solution efficiently using early stopping. Experiments show that this adaptive regularization method gives a more efficient parameter selection strategy than regularized boosting and semi supervised boosting algorithms, and significantly improves the performances of traditional AdaBoost and related methods.
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs

Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method
Predicting Seizures from Electroencephalography Recordings: A Knowledge Transfer Strategy

Epilepsy, a brain disorder afflicts nearly 1% of the world's population, is characterized by the occurrence of spontaneous seizures. For most epilepsy patients, the drugs are either not effective or produce severe side-effects. Seizure forecasting systems have the potential to help patients with epilepsy lead more normal lives. Recently multi-center clinical studies showed evidence of premonitory symptoms in 6.2% of 500 patients with epilepsy, and some interviews of epilepsy patients also found that a certain amount of patients felt "auras". All these are promising signs suggesting that seizure might be predictable. In this paper, we will study the application of deep learning techniques for seizure prediction with EEG signals. Deep learning methods have been shown to be very effective on exploring the latent structures from continuous signals and they have achieved state-of-the-art performance on speech analysis. One potential requirement for deep learning algorithms to work is a huge training set, which could be difficult for a specific medical problem. Therefore we specifically investigated a transfer learning strategy: we performed the major seizure prediction task on the data from American Epilepsy Society Seizure Prediction Challenge1, and we adopted another 6 publicly available EEG datasets2, which are not directly related to seizure prediction, as auxiliary information to pre-train the deep neural network for getting a good initial point. Our results show that with those auxiliary information, the prediction performance can be boosted. This observation is validated with different predictive models, which opens another gate for effective integration and utilization of medical data resources.
A new algorithm for character segmentation of license plate

Character segmentation is an important step in License Plate Recognition (LPR) system. There are many difficulties in this step, such as the influence of image noise, plate frame, rivet, space mark, and so on. This paper presents a new algorithm for character segmentation, using Hough transformation and the prior knowledge in horizontal and vertical segmentation respectively. Furthermore, a new object enhancement technique is used for image preprocessing. The experiment results show a good performance of this new segmentation algorithm.