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Label-Weighted graph-based learning for Semi-Supervised Classification Under Label Noise

IEEE TRANSACTIONS ON BIG DATA 2024年第1期10卷 55-65页

作者： Liang, Naiyao Yang, Zuyuan Chen, Junhang Li, Zhenni Xie, Shengli Guangdong Univ Technol Sch Automat Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Peoples R China Minist Educ Key Lab iDetectin & Mfg IoT Guangzhou 510006 Peoples R China Guangdong Hong Kong Macao Joint Lab Smart Discrete Guangzhou 510006 Peoples R China

graph-based semi-supervised learning (GSSL) is a quite important technology due to its effectiveness in practice. Existing GSSL works often treat the given labels equally and ignore the unbalance importance of labels. In some inaccurate systems, the collected labels usually contain noise (noisy labels) and the methods treating labels equally suffer from the label noise. In this article, we propose a novel label-weighted learning method on graph for semi-supervised classification under label noise, which allows considering the contribution differences of labels. In particular, the label dependency of data is revealed by graph constraints. With the help of this label dependency, the proposed method develops the strategy of adaptive label weight, where label weights are assigned to labels adaptively. Accordingly, an efficient algorithm is developed to solve the proposed optimization objective, where each subproblem has a closed-form solution. Experimental results on a synthetic dataset and several real-world datasets show the advantage of the proposed method, compared to the state-of-the-art methods.

关键词： Noise measurement Synthetic data Robustness Big Data Adaptation models Optimization Deep learning graph-based learning semi-supervised classification label noise adaptive label weight

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ContrastLOS: A graph-based Deep learning Model With Contrastive Pre-Training for Improved ICU Length-of-Stay Prediction

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IEEE ACCESS 2025年 13卷 34132-34148页

作者： Fan, Guangrui Liu, Aixiang Zhang, Chao Taiyuan Univ Sci & Technol Dept Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Shanxi Med Univ Fenyang Coll Dept Hlth Informat Management Fenyang 030001 Shanxi Peoples R China

Accurate prediction of intensive care unit (ICU) length of stay (LOS) is crucial for optimizing resource allocation and improving patient outcomes. We propose ContrastLOS, a novel graph-based deep learning model that integrates graph transformer networks with contrastive pre-training to enhance ICU LOS prediction. By constructing dynamic patient similarity graphs, ContrastLOS captures intricate inter-patient relationships and temporal dependencies. Through contrastive learning on unlabeled data, the model develops robust patient embeddings suitable for both classification and regression tasks. Experiments on three comprehensive ICU datasets-MIMIC-III, MIMIC-IV v3.0, and eICU-show that ContrastLOS consistently outperforms state-of-the-art baselines. For classification (predicting whether LOS exceeds 3 or 7 days), it achieves AUROC scores of up to 82.1%, improving performance by up to 5.2% compared to competitive methods. In regression tasks, ContrastLOS attains the lowest RMSE (2.41 days) and MAE (1.48 days) on the multi-center eICU dataset. Notably, it maintains an AUROC of 76.8% with only 10% labeled data, highlighting its effectiveness in low-resource settings. These findings suggest that ContrastLOS can serve as a robust clinical decision support tool for critical care management.

关键词： Predictive models Data models Medical services Transformers Contrastive learning MIMICs Resource management Deep learning Complexity theory Accuracy Contrastive pre-training graph-based learning healthcare AI intensive care unit (ICU) length of stay (LOS) prediction

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Utilizing Contrastive learning for graph-based Active learning of SAR Data 30

Utilizing Contrastive Learning for Graph-Based Active Learni...

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Conference on Algorithms for Synthetic Aperture Radar Imagery XXX

作者： Brown, Jason O'Neill, Riley Calder, Jeff Bertozzi, Andrea L. Univ Calif Los Angeles Dept Math 520 Portola Plaza Los Angeles CA 90095 USA Univ Minnesota Sch Math 538 Vincent Hall206 Church St SE Minneapolis MN 55455 USA

ISBN: (纸本)9781510661547;9781510661554

Automatic target recognition with synthetic aperture radar (SAR) data is a challenging image classification problem due to the difficulty in acquiring the large labeled training sets required for conventional deep learning methods. Recent work(1) addressed this problem by utilizing powerful tools in graph-based semi-supervised learning and active learning, and achieved state of the art results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset with less labeled data compared to existing techniques. A key part of the previous work was the use of unsupervised deep learning, in particular, a convolutional variational autoencoder, to embed the MSTAR images into a meaningful feature space prior to constructing the similarity graph. In this paper, we develop a contrastive SimCLR framework for feature extraction from MSTAR images by using data augmentations specific to SAR imagery. We show that our contrastive embedding results in improved performance over the variational autoencoder similarity graph method in automatic target recognition on the MSTAR dataset. We also perform a comparative study of the quality of the autoencoder and contrastive embeddings by training support vector machines (SVM) at various label rates, applying spectral clustering, and evaluating graph-cut energies, all of which show that the contrastive learning embedding is superior to the autoencoder embedding.

关键词： Contrastive learning Active learning Synthetic Aperture Radar graph-based learning

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graph-based Active learning for Semi-supervised Classification of SAR Data 29

Graph-based Active Learning for Semi-supervised Classificati...

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Conference on Algorithms for Synthetic Aperture Radar Imagery XXIX

作者： Miller, Kevin Mauro, Jack Setiadi, Jason Baca, Xoaquin Shi, Zhan Calder, Jeff Bertozzi, Andrea L. Univ Calif Los Angeles Dept Math 520 Portola Plaza Los Angeles CA 90095 USA Loyola Marymount Univ Dept Math 1 LMU Dr Los Angeles CA 90045 USA Univ Minnesota Sch Stat 313 Ford Hall224 Church St SE Minneapolis MN 55455 USA Harvey Mudd Coll Dept Comp Sci 201 Platt Blvd Claremont CA 91711 USA Univ Minnesota Sch Math 538 Vincent Hall206 Church St SE Minneapolis MN 55455 USA

ISBN: (纸本)9781510650671;9781510650664

We present a novel method for classification of Synthetic Aperture Radar (SAR) data by combining ideas from graph-based learning and neural network methods within an active learning framework. graph-based methods in machine learning are based on a similarity graph constructed from the data. When the data consists of raw images composed of scenes, extraneous information can make the classification task more difficult. In recent years, neural network methods have been shown to provide a promising framework for extracting patterns from SAR images. These methods, however, require ample training data to avoid overfitting. At the same time, such training data are often unavailable for applications of interest, such as automatic target recognition (ATR) and SAR data. We use a Convolutional Neural Network Variational Autoencoder (CNNVAE) to embed SAR data into a feature space, and then construct a similarity graph from the embedded data and apply graph-based semi-supervised learning techniques. The CNNVAE feature embedding and graph construction requires no labeled data, which reduces overfitting and improves the generalization performance of graph learning at low label rates. Furthermore, the method easily incorporates a human-in-the-loop for active learning in the data-labeling process. We present promising results and compare them to other standard machine learning methods on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset for ATR with small amounts of labeled data.

关键词： Active learning Synthetic Aperture Radar graph-based learning

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Is This Class Thread-Safe? Inferring Documentation using graph-based learning 18

Is This Class Thread-Safe? Inferring Documentation using Gra...

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33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Habib, Andrew Pradel, Michael Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany

ISBN: (数字)9781450359375

ISBN: (纸本)9781450359375

Thread-safe classes are pervasive in concurrent, object-oriented software. However, many classes lack documentation regarding their safety guarantees under multi-threaded usage. This lack of documentation forces developers who use a class in a concurrent program to either carefully inspect the implementation of the class, to conservatively synchronize all accesses to it, or to optimistically assume that the class is thread-safe. To overcome the lack of documentation, we present TSFinder, an approach to automatically classify classes as supposedly thread-safe or thread-unsafe. The key idea is to combine a lightweight static analysis that extracts a graph representation from classes with a graph-based classifier. After training the classifier with classes known to be thread-safe and thread-unsafe, it achieves an accuracy of 94.5% on previously unseen classes, enabling the approach to infer thread safety documentation with high confidence. The classifier takes about 3 seconds per class, i.e., it is efficient enough to infer documentation for many classes.

关键词： inferring documentation thread-safe class graph-based learning

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Efficient locality weighted sparse representation for graph-based learning

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KNOWLEDGE-based SYSTEMS 2017年第Apr.1期121卷 129-141页

作者： Feng, Xiaodong Wu, Sen Zhou, Wenjun Quan, Min Univ Elect Sci & Technol China Sch Polit Sci & Publ Adm Chengdu 611731 Sichuan Peoples R China Univ Sci & Technol Beijing Donlinks Sch Econ & Management Beijing 100083 Peoples R China Univ Tennessee Business Analyt & Stat Knoxville TN 37996 USA

Constructing a graph to represent the structure among data objects plays a fundamental role in various data mining tasks with graph-based learning. Since traditional pairwise distance-based graph construction is sensitive to noise and outliers, sparse representation based graphs (e.g., l(1)-graphs) have been proposed in the literature. Although l(1)-graphs prove powerful and robust for many graph-based learning tasks, it suffers from weak locality and high computation costs. In this paper, we propose a locality weighted sparse representation (LWSR), which aims for good preservation of the locality structure among data objects and a significant reduction of the computation time. LWSR approximates each object as a sparse linear combination of its nearest neighbors, and weights their corresponding coefficients by their distances to the target object. Experimental results show that LWSR-graph based learning methods outperform state-of-the-art methods in both effectiveness and efficiency for graph-based learning. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Sparse representation Locality preserving graph-based learning graph construction

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Adaptive edge weighting for graph-based learning algorithms

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MACHINE learning 2017年第2期106卷 307-335页

作者： Karasuyama, Masayuki Mamitsuka, Hiroshi Nagoya Inst Technol Dept Engn Showa Ku Nagoya Aichi 4668555 Japan Kyoto Univ Inst Chem Res Bioinformat Ctr Uji Kyoto 6110011 Japan Aalto Univ Dept Comp Sci Espoo 02150 Finland

graph-based learning algorithms including label propagation and spectral clustering are known as the effective state-of-the-art algorithms for a variety of tasks in machine learning applications. Given input data, i.e. feature vectors, graph-based methods typically proceed with the following three steps: (1) generating graph edges, (2) estimating edge weights and (3) running a graph based algorithm. The first and second steps are difficult, especially when there are only a few (or no) labeled instances, while they are important because the performance of graph-based methods heavily depends on the quality of the input graph. For the second step of the three-step procedure, we propose a new method, which optimizes edge weights through a local linear reconstruction error minimization under a constraint that edges are parameterized by a similarity function of node pairs. As a result our generated graph can capture the manifold structure of the input data, where each edge represents similarity of each node pair. To further justify this approach, we also provide analytical considerations for our formulation such as an interpretation as a cross-validation of a propagation model in the feature space, and an error analysis based on a low dimensional manifold model. Experimental results demonstrated the effectiveness of our adaptive edge weighting strategy both in synthetic and real datasets.

关键词： graph-based learning Manifold assumption Edge weighting Semi-supervised learning Clustering

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A Three-Layered graph-based learning Approach for Remote Sensing Image Retrieval

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2016年第10期54卷 6020-6034页

作者： Wang, Yuebin Zhang, Liqiang Tong, Xiaohua Zhang, Liang Zhang, Zhenxin Liu, Hao Xing, Xiaoyue Mathiopoulos, P. Takis Beijing Normal Univ State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China Tongji Univ Sch Surveying & Geoinformat Shanghai 200092 Peoples R China Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens 15784 Greece

With the emergence of huge volumes of high-resolution remote sensing images produced by all sorts of satellites and airborne sensors, processing and analysis of these images require effective retrieval techniques. To alleviate the dramatic variation of the retrieval accuracy among queries caused by the single image feature algorithms, we developed a novel graph-based learning method for effectively retrieving remote sensing images. The method utilizes a three-layer framework that integrates the strengths of query expansion and fusion of holistic and local features. In the first layer, two retrieval image sets are obtained by, respectively, using the retrieval methods based on holistic and local features, and the top-ranked and common images from both of the top candidate lists subsequently form graph anchors. In the second layer, the graph anchors as an expansion query retrieve six image sets from the image database using each individual feature. In the third layer, the images in the six image sets are evaluated for generating positive and negative data, and SimpleMKL is applied to learn suitable query-dependent fusion weights for achieving the final image retrieval result. Extensive experiments were performed on the UC Merced Land Use-Land Cover data set. The source code has been available at our website. Compared with other related methods, the retrieval precision is significantly enhanced without sacrificing the scalability of our approach.

关键词： Expansion query graph-based learning image retrieval query fusion reranking

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graph-based ACTIVE learning: A NEW LOOK AT EXPECTED ERROR MINIMIZATION

GRAPH-BASED ACTIVE LEARNING: A NEW LOOK AT EXPECTED ERROR MI...

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Jun, Kwang-Sung Nowak, Robert Univ Wisconsin Madison Wisconsin Inst Discovery Madison WI 53706 USA

ISBN: (纸本)9781509045457

In graph-based active learning, algorithms based on expected error minimization (EEM) have been popular and yield good empirical performance. The exact computation of EEM optimally balances exploration and exploitation. In practice, however, EEM-based algorithms employ various approximations due to the computational hardness of exact EEM. This can result in a lack of either exploration or exploitation, which can negatively impact the effectiveness of active learning. We propose a new algorithm TSA (Two-Step Approximation) that balances between exploration and exploitation efficiently while enjoying the same computational complexity as existing approximations. Finally, we empirically show the value of balancing between exploration and exploitation in both toy and real-world datasets where our method outperforms several state-of-the-art methods.

关键词： Machine learning active learning semi supervised learning graph-based learning probabilistic model

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A graph-based approach for positive and unlabeled learning

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INFORMATION SCIENCES 2021年 580卷 655-672页

作者： Carnevali, Julio Cesar Rossi, Rafael Geraldeli Milios, Evangelos Lopes, Alneu de Andrade Univ Sao Paulo Inst Math & Comp Sci Sao Paulo SP Brazil Univ Fed Mato Grosso do Sul Campo Grande MS Brazil Dalhousie Univ Halifax NS Canada

Positive and Unlabeled learning (PUL) uses unlabeled documents and a few positive documents for retrieving a set of "interest" documents from a text collection. Usually, PUL approaches are based on the vector space model. However, when dealing with semisupervised learning for text classification or information retrieval, graph-based approaches have been proved to outperform vector space model-based approaches. So, in this article, a graph-based approach for PUL is proposed: Label Propagation for Positive and Unlabeled learning (LP-PUL). The proposed framework consists of three steps: (i) building a similarity graph, (ii) identifying reliable negative documents, and (iii) performing label propagation to classify the remaining unlabeled documents as positive or negative. We carried out experiments to measure the impact of the different choices in each step of the proposed framework. We also demonstrated that the proposal surpasses the classification performance of other PUL (RC-SVM, PU-LP, and PE-PUC) or one-class learning (k-NN-based, kMeans-based, and Dense Autoencoder) algorithms in terms of F-1. Considering the best results of any algorithm used in the experimental evaluation, PU-PUL can improve the classification performance from 2%, when using only 1 labeled document, to 28%, when 30 labeled documents are employed. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Positive and unlabeled learning Text classification Label propagation Transductive learning graph-based learning

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