Large Number Of Unlabeled Samples Research Articles

An Analysis Model of Sports Human Body Based on Computer Vision Tracking Technology

This paper proposes the analysis model of sports human body based on computer vision tracking technology. Visual target tracking is an important research field of the computer vision, motion trajectory and it can provide not only the goal, to provide the initial data movement analysis, scene understanding, behavior or the event detection in intelligent surveillance, human-computer interaction, robot visual navigation and motion recognition based on field has a broad application prospect. For this reason, it is possible to consider the use of a large number of unlabeled samples to assist the training classifier to improve its performance. This type of machine learning method using both labeled and that unlabeled samples is called the semi-supervised learning. This paper proposes the novel idea of the related research topics to propose the new perspective of the model that will be later give us the novel idea of making it efficient for further development of sport science.

Assisted Diagnosis Research Based on Improved Deep Autoencoder

Deep Autoencoder has the powerful ability to learn features from large number of unlabeled samples and a small number of labeled samples. In this work, we have improved the network structure of the general deep autoencoder and applied it to the disease auxiliary diagnosis. We have achieved a network by entering the specific indicators and predicting whether suffering from liver disease, the network using real physical examination data for training and verification. Compared with the traditional semi-supervised machine learning algorithm, deep autoencoder will get higher accuracy.

A Novel Active Learning Method Using SVM for Text Classification

Support vector machines (SVMs) are a popular class of supervised learning algorithms, and are particularly applicable to large and high-dimensional classification problems. Like most machine learning methods for data classification and information retrieval, they require manually labeled data samples in the training stage. However, manual labeling is a time consuming and errorprone task. One possible solution to this issue is to exploit the large number of unlabeled samples that are easily accessible via the internet. This paper presents a novel active learning method for text categorization. The main objective of active learning is to reduce the labeling effort, without compromising the accuracy of classification, by intelligently selecting which samples should be labeled. The proposed method selects a batch of informative samples using the posterior probabilities provided by a set of multi-class SVM classifiers, and these samples are then manually labeled by an expert. Experimental results indicate that the proposed active learning method significantly reduces the labeling effort, while simultaneously enhancing the classification accuracy.

A non-negative sparse semi-supervised dimensionality reduction algorithm for hyperspectral data

A non-negative sparse semi-supervised dimensionality reduction algorithm is proposed for hyperspectral data by making adequate use of a few labeled samples and a large number of unlabeled samples. The objective function of the proposed algorithm consists of two terms: (1) a discriminant item is designed to analyze a few labeled samples from the global viewpoint, which can assess the separability between surface objects; (2) a regularization term is used to build a non-negative sparse representation graph based on the unlabeled samples, which can adaptively find an adjacency graph for each sample and then find valuable samples with huge information volume from the original hyperspectral data. Based on the objective function and the maximum margin criterion, a dimensionality reduction algorithm, the non-negative sparse semi-supervised maximum margin algorithm, is proposed. Experimental results on the ROSIS University and AVIRIS 92AV3C hyperspectral data sets show that the proposed algorithm can effectively utilize the unlabeled samples to achieve higher overall classification accuracy and Kappa coefficient when compared with some representative supervised, unsupervised and semi-supervised dimensionality reduction algorithms.

Text Categorization using Reduced Training Set

The machine learning approaches to text categorization proceed by teaching the system how to classify through labeled samples. In real application scenarios, the collection of training (labeled) samples to design a classifier is not always trivial due to the complexity and the cost which characterize the process. A possible solution to this issue can be found in the exploitation of the large number of unlabeled samples which are accessible at zero cost from the web. Active learning strives to reduce the required labeling effort while retaining the accuracy by intelligently selecting the samples to be labeled. This Study presents a novel active learning method for text classification that selects a batch of informative samples for manual labeling by an expert. The proposed method uses the posterior probability output of a multi-class SVM method. The experiments are performed with two well-known datasets and the presented experimental results show that employing our active learning method can significantly reduce the need for labeled training data.

Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning

Study on land use/cover can reflect changing rules of population, economy, agricultural structure adjustment, policy, and traffic and provide better service for the regional economic development and urban evolution. The study on fine land use/cover assessment using hyperspectral image classification is a focal growing area in many fields. Semisupervised learning method which takes a large number of unlabeled samples and minority labeled samples, improving classification and predicting the accuracy effectively, has been a new research direction. In this paper, we proposed improving fine land use/cover assessment based on semisupervised hyperspectral classification method. The test analysis of study area showed that the advantages of semisupervised classification method could improve the high precision overall classification and objective assessment of land use/cover results.

A New Semi-supervised Classification Method of Hyperspectral Image based on Combining Renyi Entropy and Multinomial Logistic Regression Algorithm

The study on classification methods of hyperspectral image is a focal growing area in remote sensing applications because the wide spectral range, providing a very high spectral resolution, allows the detection and classification surfaces and chemical elements of the observed image. Semi-supervised learning method which takes a large number of unlabeled samples and minority labeled samples, improving classification and predicting the accuracy effectively have been a new research direction. In this paper we proposed a new semisupervised classification method of hyperspectral image based on combining Renyi entropy and multinomial logistic regression algorithm. The multinomial logistic regression was performed to describe a direct relationship between the selected sample as and their category. A lot of unlabeled samples are constantly added to the sample data using Renyi entropy algorithm. The test analysis of image classification in test area showed the advantages of classification method based on combining Renyi entropy and multinomial logistic regression algorithm for hyperspectral remote sensing image.

Two-way Markov random walk transductive learning algorithm

Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk (TMRW) algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.

Semisupervised Dimensionality Reduction of Hyperspectral Images via Local Scaling Cut Criterion

Hyperspectral images (HSIs) provide a vast amount of geometrical, radiation, and spectral information about a scene. However, high-dimensional data make HSI classification complex and time consuming. It is important to reduce the dimensionality and find a low-dimensional representation of the high-dimensional data. Since the labels of HSI data are really difficult to collect while the unlabeled data are abundant and easy to obtain, in this letter, a semisupervised dimensionality reduction method using both limited labeled samples and a large number of unlabeled samples based on a local scaling cut (LSC) criterion is proposed. LSC is similar to linear discriminant analysis (LDA), but it can handle the heteroscedastic and multimodal data for which LDA fails. The framework of our proposed method contains two terms: 1) a discrimination term based on the labeled samples and 2) a regularization term based on the prior knowledge provided by both labeled and unlabeled samples. Experimental results show that our proposed algorithm provides a relatively promising performance compared with other methods. Moreover, the algorithm is stable and insensitive to parameters.

Learning Prototype Hyperplanes for Face Verification in the Wild

In this paper, we propose a new scheme called Prototype Hyperplane Learning (PHL) for face verification in the wild using only weakly labeled training samples (i.e., we only know whether each pair of samples are from the same class or different classes without knowing the class label of each sample) by leveraging a large number of unlabeled samples in a generic data set. Our scheme represents each sample in the weakly labeled data set as a mid-level feature with each entry as the corresponding decision value from the classification hyperplane (referred to as the prototype hyperplane) of one Support Vector Machine (SVM) model, in which a sparse set of support vectors is selected from the unlabeled generic data set based on the learnt combination coefficients. To learn the optimal prototype hyperplanes for the extraction of mid-level features, we propose a Fisher’s Linear Discriminant-like (FLD-like) objective function by maximizing the discriminability on the weakly labeled data set with a constraint enforcing sparsity on the combination coefficients of each SVM model, which is solved by using an alternating optimization method. Then, we use the recent work called Side-Information based Linear Discriminant (SILD) analysis for dimensionality reduction and a cosine similarity measure for final face verification. Comprehensive experiments on two data sets, Labeled Faces in the Wild (LFW) and YouTube Faces, demonstrate the effectiveness of our scheme.

Indoor Location Estimation with Reduced Calibration Exploiting Unlabeled Data via Hybrid Generative/Discriminative Learning

For indoor location estimation based on wireless local area networks fingerprinting, how to reduce the offline calibration effort while maintaining high location estimation accuracy is of major concern. In this paper, a hybrid generative/discriminative semi-supervised learning algorithm is proposed that utilizes a large number of unlabeled samples to supplement a small number of labeled samples. This hybrid method allows us to combine the modeling power and flexibility of generative models with the superior performance of discriminative approaches. Other related issues, such as learning efficiency enhancement and distribution estimation smoothing, are also discussed. Extensive experimental results show that our proposed method can effectively reduce the calibration effort and exhibit superior performance in terms of localization accuracy and robustness.

SELM: Semi-supervised ELM with application in sparse calibrated location estimation

Indoor location estimation based on Wi-Fi has attracted more and more attention from both research and industry fields. It brings two significant challenges. One is requiring a vast amount of labeled calibration data. The other is real-time training and testing for location estimation task. Traditional machine learning methods cannot get high performance in both aspects. This paper proposed a novel semi-supervised learning method SELM (semi-supervised extreme learning machine) and applied it to sparse calibrated location estimation. There are two advantages of the proposed SELM. First, it employs graph Laplacian regularization to import large number of unlabeled samples which can dramatically reduce labeled calibration samples. Second, it inherits the good property of ELM on extreme training and testing speed. Comparative experiments show that with same number of labeled samples, our method outperforms original ELM and back propagation (BP) network, especially in the case that the calibration data is very sparse.

Semisupervised Learning for a Hybrid Generative/Discriminative Classifier based on the Maximum Entropy Principle

This paper presents a method for designing semi-supervised classifiers trained on labeled and unlabeled samples. We focus on probabilistic semi-supervised classifier design for multi-class and single-labeled classification problems, and propose a hybrid approach that takes advantage of generative and discriminative approaches. In our approach, we first consider a generative model trained by using labeled samples and introduce a bias correction model, where these models belong to the same model family, but have different parameters. Then, we construct a hybrid classifier by combining these models based on the maximum entropy principle. To enable us to apply our hybrid approach to text classification problems, we employed naive Bayes models as the generative and bias correction models. Our experimental results for four text data sets confirmed that the generalization ability of our hybrid classifier was much improved by using a large number of unlabeled samples for training when there were too few labeled samples to obtain good performance. We also confirmed that our hybrid approach significantly outperformed generative and discriminative approaches when the performance of the generative and discriminative approaches was comparable. Moreover, we examined the performance of our hybrid classifier when the labeled and unlabeled data distributions were different.

A Hybrid Generative/Discriminative Classifier Design for Semi-supervised Learing

Semi-supervised classifier design that simultaneously utilizes both a small number of labeled samples and a large number of unlabeled samples is a major research issue in machine learning. Existing semi-supervised learning methods for probabilistic classifiers belong to either generative or discriminative approaches. This paper focuses on a semi-supervised probabilistic classifier design for multiclass and single-labeled classification problems and first presents a hybrid approach to take advantage of the generative and discriminative approaches. Our formulation considers a generative model trained on labeled samples and a newly introduced bias correction model, whose belongs to the same model family as the generative model, but whose parameters are different from the generative model. A hybrid classifier is constructed by combining both the generative and bias correction models based on the maximum entropy principle, where the combination weights of these models are determined so that the class labels of labeled samples are as correctly predicted as possible. We apply the hybrid approach to text classification problems by employing naive Bayes as the generative and bias correction models. In our experimental results on three English and one Japanese text data sets, we confirmed that the hybrid classifier significantly outperformed conventional probabilistic generative and discriminative classifiers when the classification performance of the generative classifier was comparable to the discriminative classifier.

Statistical modeling and conceptualization of natural images

Multi-level annotation of images is a promising solution to enable semantic image retrieval by using various keywords at different semantic levels. In this paper, we propose a multi-level approach to interpret and annotate the semantics of natural images by using both the dominant image components and the relevant semantic image concepts. In contrast to the well-known image-based and region-based approaches, we use the concept-sensitive salient objects as the dominant image components to achieve automatic image annotation at the content level. By using the concept-sensitive salient objects for image content representation and feature extraction, a novel image classification technique is developed to achieve automatic image annotation at the concept level. To detect the concept-sensitive salient objects automatically, a set of detection functions are learned from the labeled image regions by using support vector machine (SVM) classifiers with an automatic scheme for searching the optimal model parameters. To generate the semantic image concepts, the finite mixture models are used to approximate the class distributions of the relevant concept-sensitive salient objects. An adaptive EM algorithm has been proposed to determine the optimal model structure and model parameters simultaneously. In addition, a large number of unlabeled samples have been integrated with a limited number of labeled samples to achieve more effective classifier training and knowledge discovery. We have also demonstrated that our algorithms are very effective to enable multi-level interpretation and annotation of natural images.

Robust parameter estimation for mixture model

In pattern recognition, when the ratio of the number of training samples to the dimensionality is small, parameter estimates become highly variable, causing the deterioration of classification performance. This problem has become more prevalent in remote sensing with the emergence of a new generation of sensors with as many as several hundred spectral bands. While the new sensor technology provides higher spectral and spatial resolution, enabling a greater number of spectrally separable classes to be identified, the needed labeled samples for designing the classifier remain difficult and expensive to acquire. Better parameter estimates can be obtained by exploiting a large number of unlabeled samples in addition to training samples, using the expectation maximization algorithm under the mixture model. However, the estimation method is sensitive to the presence of statistical outliers. In remote sensing data, miscellaneous classes with few samples are often difficult to identify and may constitute statistical outliers. Therefore, the authors propose to use a robust parameter-estimation method for the mixture model. The proposed method assigns full weight to training samples, but automatically gives reduced weight to unlabeled samples. Experimental results show that the robust method prevents performance deterioration due to statistical outliers in the data as compared to the estimates obtained from the direct EM approach.