A variety of modern applications exhibit multi-view multi-label learning, where each sample has multi-view features, and multiple labels are correlated via common views. Current methods usually fail to directly deal with the setting where only a subset of features and labels are observed for each sample, and ignore the presence of noisy views and imbalanced labels in real-world problems. In this paper, we propose a novel method to overcome the limitations. It jointly embeds incomplete views and weak labels into a low-dimensional subspace with adaptive weights, and facilitates the difference between embedding weight matrices via auto-weighted Hilbert-Schmidt Independence Criterion (HSIC) to reduce the redundancy. Moreover, it adaptively learns view-wise importance for embedding to detect noisy views, and mitigates the label imbalance problem by focal loss. Experimental results on four real-world multi-view multi-label datasets demonstrate the effectiveness of the proposed method.
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
Center for Imaging Science, Rochester Institute of Technology（罗切斯特理工学院成像科学中心）；University of Aberdeen（阿伯丁大学）；Biomedical Engineering, Rochester Institute of Technology（罗切斯特理工学院生物医学工程系）
The paper introduces a new dataset to assess the performance of machine learning algorithms in the prediction of the seriousness of injury in a traffic accident. The dataset is created by aggregating publicly available datasets from the UK Department for Transport, which are drastically imbalanced with missing attributes sometimes approaching 50\% of the overall data dimensionality. The paper presents the data analysis pipeline starting from the publicly available data of road traffic accidents and ending with predictors of possible injuries and their degree of severity. It addresses the huge …
Learning against label noise is a vital topic to guarantee a reliable performance for deep neural networks. Recent research usually refers to dynamic noise modeling with model output probabilities and loss values, and then separates clean and noisy samples. These methods have gained notable success. However, unlike cherry-picked data, existing approaches often cannot perform well when facing imbalanced datasets, a common scenario in the real world. We thoroughly investigate this phenomenon and point out two major issues that hinder the performance, i.e., \emph{inter-class loss distribution dis…
Supervised learning algorithms are heavily reliant on annotated datasets to train machine learning models. However, the curation of the annotated datasets is laborious and time consuming due to the manual effort involved and has become a huge bottleneck in supervised learning. In this work, we apply the theory of noisy learning to generate weak supervision signals instead of manual annotation. We curate a noisy labeled dataset using a labeling heuristic to identify epidemic related tweets. We evaluated the performance using a large epidemic corpus and our results demonstrate that models traine…
Enhancing stop location detection for incomplete urban mobility datasets
IMT School for Advanced Studies Lucca（卢卡IMT高等研究院）；Carlos III University of Madrid（马德里卡洛斯三世大学）；Caja Blanca Datos SL（Caja Blanca Datos SL公司）；University of Amsterdam（阿姆斯特丹大学）；University of Zaragoza（萨拉戈萨大学）；University of Padua（帕多瓦大学）；Cuebiq Inc.（Cuebiq公司）
National Research University Higher School of Economics（国立研究型大学高等经济学院）；Laboratory of Methods for Big Data Analysis（大数据分析方法实验室）
Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets and compared to a set of conventional one-class and two-class approaches.
The University of Sydney（悉尼大学）；Medicinovo Inc.（Medicinovo公司）
In medical real-world study (RWS), how to fully utilize the fragmentary and scarce information in model training to generate the solid diagnosis results is a challenging task. In this work, we introduce a novel multi-instance neural network, AMI-Net+, to train and predict from the incomplete and extremely imbalanced data. It is more effective than the state-of-art method, AMI-Net. First, we also implement embedding, multi-head attention and gated attention-based multi-instance pooling to capture the relations of symptoms themselves and with the given disease. Besides, we propose var-ious impro…
Politecnico di Milano（米兰理工大学）；University of Victoria（维多利亚大学）；Swiss Federal Institute of Technology at Lausanne (EPFL)（洛桑联邦理工学院（EPFL））
Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing…
Nanjing University（南京大学）；Tencent AI Lab（腾讯人工智能实验室）
Learning with Noisy Labels (LNL) has attracted significant attention from the research community. Many recent LNL methods rely on the assumption that clean samples tend to have "small loss". However, this assumption always fails to generalize to some real-world cases with imbalanced subpopulations, i.e., training subpopulations varying in sample size or recognition difficulty. Therefore, recent LNL methods face the risk of misclassifying those "informative" samples (e.g., hard samples or samples in the tail subpopulations) into noisy samples, leading to poor generalization performance. To addr…
Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Terminus Group（特步集团）；School of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）
Technical University of Munich (TUM)（慕尼黑工业大学）；German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-Universität (LMU)（路德维希-马克西米利安大学（LMU）德国眩晕与平衡障碍中心（DSGZ））；Johns Hopkins University（约翰斯·霍普金斯大学）
Clinical diagnostic decision making and population-based studies often rely on multi-modal data which is noisy and incomplete. Recently, several works proposed geometric deep learning approaches to solve disease classification, by modeling patients as nodes in a graph, along with graph signal processing of multi-modal features. Many of these approaches are limited by assuming modality- and feature-completeness, and by transductive inference, which requires re-training of the entire model for each new test sample. In this work, we propose a novel inductive graph-based approach that can generali…
With growing credit card transaction volumes, the fraud percentages are also rising, including overhead costs for institutions to combat and compensate victims. The use of machine learning into the financial sector permits more effective protection against fraud and other economic crime. Suitably trained machine learning classifiers help proactive fraud detection, improving stakeholder trust and robustness against illicit transactions. However, the design of machine learning based fraud detection algorithms has been challenging and slow due the massively unbalanced nature of fraud data and the…
Probabilistic approach to Boolean matrix factorization can provide solutions robustagainst noise and missing values with linear computational complexity. However,the assumption about latent factors can be problematic in real world applications.This study proposed a new probabilistic algorithm free of assumptions of latentfactors, while retaining the advantages of previous algorithms. Real data experimentshowed that our algorithm was favourably compared with current state-of-the-artprobabilistic algorithms.
School of Computing Science, Newcastle University（纽卡斯尔大学计算科学学院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Merck（默克公司）；Bioscience Institute, Newcastle University（纽卡斯尔大学生物科学研究所）；Institut de Recherches Internationales Servier（赛诺菲国际研究所）；GlaxoSmithKline（葛兰素史克公司）；Delft University of Technology（代尔夫特理工大学）；State Research Institute Centre for Innovative Medicine（创新医学国家研究中心）；University of Oulu（奥卢大学）；Versus Arthritis（对抗关节炎组织）
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class class…
University of Edinburgh（爱丁堡大学）；University of Washington（华盛顿大学）
Agents learning to act autonomously in real-world domains must acquire a model of the dynamics of the domain in which they operate. Learning domain dynamics can be challenging, especially where an agent only has partial access to the world state, and/or noisy external sensors. Even in standard STRIPS domains, existing approaches cannot learn from noisy, incomplete observations typical of real-world domains. We propose a method which learns STRIPS action models in such domains, by decomposing the problem into first learning a transition function between states in the form of a set of classifier…
We consider the problem of learning the true ordering of a set of alternatives from largely incomplete and noisy rankings. We introduce a natural generalization of both the classical Mallows model of ranking distributions and the extensively studied model of noisy pairwise comparisons. Our selective Mallows model outputs a noisy ranking on any given subset of alternatives, based on an underlying Mallows distribution. Assuming a sequence of subsets where each pair of alternatives appears frequently enough, we obtain strong asymptotically tight upper and lower bounds on the sample complexity of …
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
In real-world scenarios, the observation data for reinforcement learning with continuous control is commonly noisy and part of it may be dynamically missing over time, which violates the assumption of many current methods developed for this. We addressed the issue within the framework of partially observable Markov Decision Process (POMDP) using a model-based method, in which the transition model is estimated from the incomplete and noisy observations using a newly proposed surrogate loss function with local approximation, while the policy and value function is learned with the help of belief …
A main challenge in target localization arises from the lack of reliable distance measures. This issue is especially pronounced in indoor settings due to the presence of walls, floors, furniture, and other dynamically changing conditions such as the movement of people and goods, varying temperature, and airflows. Here, we develop a new computational framework to estimate the location of a target without the need for reliable distance measures. The method, which we term Ordinal UNLOC, uses only ordinal data obtained from comparing the signal strength from anchor pairs at known locations to the …
Understanding and Mitigating the Bias in Sample Selection for Learning with Noisy Labels
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Singapore University of Technology and Design（新加坡科技设计大学）；School of Software, Zhejiang University（浙江大学软件学院）
We study a practical algorithm for sparse principal component analysis (PCA) of incomplete and noisy data. Our algorithm is based on the semidefinite program (SDP) relaxation of the non-convex $l_1$-regularized PCA problem. We provide theoretical and experimental evidence that SDP enables us to exactly recover the true support of the sparse leading eigenvector of the unknown true matrix, despite only observing an incomplete (missing uniformly at random) and noisy version of it. We derive sufficient conditions for exact recovery, which involve matrix incoherence, the spectral gap between the la…
Towards Better Performance in Incomplete LDL: Addressing Data Imbalance
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（教育部新一代人工智能技术及其交叉应用重点实验室（东南大学））；School of Computing & Information Sciences, Caritas Institute of Higher Education（明爱专上学院计算与信息科学学院）
Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data
School of Computation, Information and Technology, Technical University of Munich, Germany（计算、信息与技术学院，慕尼黑技术大学，德国）；Department of Economics, Virginia Tech, USA（经济系，弗吉尼亚理工学院，美国）；Munich Institute of Robotics and Machine Intelligence（慕尼黑机器人与人工智能研究所）
Beijing Institute of Technology（北京理工大学）；Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China（光电成像技术与系统教育部重点实验室）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）
Corrupted labels and class imbalance are commonly encountered in practically collected training data, which easily leads to over-fitting of deep neural networks (DNNs). Existing approaches alleviate these issues by adopting a sample re-weighting strategy, which is to re-weight sample by designing weighting function. However, it is only applicable for training data containing only either one type of data biases. In practice, however, biased samples with corrupted labels and of tailed classes commonly co-exist in training data. How to handle them simultaneously is a key but under-explored proble…
GK-SMOTE: A Hyperparameter-free Noise-Resilient Gaussian KDE-Based Oversampling Approach
College of Computer Science, Chongqing University, Chongqing 400044, China（重庆大学计算机学院）；Department of Computer Science, Michigan Technological University, Houghton, MI 49931, Michigan, United States（密歇根技术大学计算机系）
ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering
Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology（大数据安全湖北工程研究中心，信息科学与工程学院，华中科技大学）；School of Computer Science and Technology, Huazhong University of Science and Technology（计算机科学与技术学院，华中科技大学）；Key Laboratory of Information Storage System Ministry of Education of China, Huazhong University of Science and Technology（信息存储系统教育部重点实验室，华中科技大学）；Department of Electrical and Computer Engineering, University of Florida（电气与计算机工程系，佛罗里达大学）
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data
Computer Engineering Department, İstanbul Commerce University（伊斯坦布尔商业大学计算机工程系）；Turkcell Teknoloji（Turkcell技术公司）；Grid Communications and Security Group, Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory（橡树岭国家实验室电网安全与能源基础设施部门）
针对联邦学习中的噪声标签、类别缺失与不均衡问题，提出结合自适应噪声清洗、条件 GAN 合成补全和鲁棒训练的隐私保护框架，并在 MNIST 与 Fashion-MNIST 上验证其性能提升。
Extracting Clean and Balanced Subset for Noisy Long-tailed Classification
Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；CSIRO’s Data61（联邦科学与工业研究组织数据61部门）；The University of Sydney（悉尼大学）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）
Peking University（北京大学）；Southern University of Science and Technology（南方科技大学）；Peng Cheng Laboratory（鹏城实验室）
Data-driven methods have recently made great progress in the discovery of partial differential equations (PDEs) from spatial-temporal data. However, several challenges remain to be solved, including sparse noisy data, incomplete candidate library, and spatially- or temporally-varying coefficients. In this work, a new framework, which combines neural network, genetic algorithm and adaptive methods, is put forward to address all of these challenges simultaneously. In the framework, a trained neural network is utilized to calculate derivatives and generate a large amount of meta-data, which solve…
We use Generative Adversarial Networks (GANs) to design a class conditional label noise (CCN) robust scheme for binary classification. It first generates a set of correctly labelled data points from noisy labelled data and 0.1% or 1% clean labels such that the generated and true (clean) labelled data distributions are close; generated labelled data is used to learn a good classifier. The mode collapse problem while generating correct feature-label pairs and the problem of skewed feature-label dimension ratio ($\sim$ 784:1) are avoided by using Wasserstein GAN and a simple data representation c…
Gamma hadron classification, a central machine learning task in gamma ray astronomy, is conventionally tackled with supervised learning. However, the supervised approach requires annotated training data to be produced in sophisticated and costly simulations. We propose to instead solve gamma hadron classification with a noisy label approach that only uses unlabeled data recorded by the real telescope. To this end, we employ the significance of detection as a learning criterion which addresses this form of weak supervision. We show that models which are based on the significance of detection de…
Guangdong University of Technology（广东工业大学）；RIKEN（理化学研究所）
Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make the explicit or implicit assumption that the observed entries are noise-free to provide a theoretical guarantee of exact recovery of missing entries, which is quite restrictive in practice. To remedy such drawbacks, this paper proposes a novel noisy tensor completion model, which complements the incompetence of existing works in handling the degeneration of high-order and noisy observations. Specifically, the tensor r…
The remarkable success of today's deep neural networks highly depends on a massive number of correctly labeled data. However, it is rather costly to obtain high-quality human-labeled data, leading to the active research area of training models robust to noisy labels. To achieve this goal, on the one hand, many papers have been dedicated to differentiating noisy labels from clean ones to increase the generalization of DNN. On the other hand, the increasingly prevalent methods of self-semi-supervised learning have been proven to benefit the tasks when labels are incomplete. By 'semi' we regard t…
This paper is concerned with estimating the column space of an unknown low-rank matrix $\boldsymbol{A}^{\star}\in\mathbb{R}^{d_{1}\times d_{2}}$, given noisy and partial observations of its entries. There is no shortage of scenarios where the observations -- while being too noisy to support faithful recovery of the entire matrix -- still convey sufficient information to enable reliable estimation of the column space of interest. This is particularly evident and crucial for the highly unbalanced case where the column dimension $d_{2}$ far exceeds the row dimension $d_{1}$, which is the focal po…
The University of Sydney（悉尼大学）；Renmin University of China（中国人民大学）；Beijing Medicinovo Technology Co.,Ltd.（北京美迪诺沃科技有限公司）
One way to extract patterns from clinical records is to consider each patient record as a bag with various number of instances in the form of symptoms. Medical diagnosis is to discover informative ones first and then map them to one or more diseases. In many cases, patients are represented as vectors in some feature space and a classifier is applied after to generate diagnosis results. However, in many real-world cases, data is often of low-quality due to a variety of reasons, such as data consistency, integrity, completeness, accuracy, etc. In this paper, we propose a novel approach, attentio…
