在现实世界的机器学习场景中，处理不完全异构数据仍是核心挑战：缺失值可能遵循复杂机制（MCAR完全随机缺失、MAR随机缺失、MNAR非随机缺失），且特征可能为混合类型（数值型和分类型）。现有方法通常依赖插补，可能引入偏差或隐私风险，也无法同时应对数据异构性和结构化缺失问题。我们提出异构不完全概率质量核（Heterogeneous Incomplete Probability Mass Kernel，简称HI-PMK），这是一种无需插补的新型数据依赖表示学习方法。HI-PMK包含两项关键创新：（1）基于概率质量的相异性度量，可适配异构特征（数值型、有序型、名义型）的局部数据分布；（2）缺失感知不确定性策略（MaxU），通过为未观测条目分配最大合理相异性，保守地适配全部三种缺失机制。该方法具备隐私保护性、可扩展性，可直接应用于分类、聚类等下游任务。在超过15个基准数据集上的大量实验表明，在各类缺失数据设置下，HI-PMK的性能始终优于传统基于插补的流程和核方法。代码可在https://github.com/echoid/Incomplete-Heter-Kernel获取。
英文摘要：
Handling incomplete and heterogeneous data remains a central challenge in real-world machine learning, where missing values may follow complex mechanisms (MCAR, MAR, MNAR) and features can be of mixed types (numerical and categorical). Existing methods often rely on imputation, which may introduce bias or privacy risks, or fail to jointly address data heterogeneity and structured missingness. We propose the \textbf{H}eterogeneous \textbf{I}ncomplete \textbf{P}robability \textbf{M}ass \textbf{K}ernel (\textbf{HI-PMK}), a novel data-dependent representation learning approach that eliminates the need for imputation. HI-PMK introduces two key innovations: (1) a probability mass-based dissimilarity measure that adapts to local data distributions across heterogeneous features (numerical, ordinal, nominal), and (2) a missingness-aware uncertainty strategy (MaxU) that conservatively handles all three missingness mechanisms by assigning maximal plausible dissimilarity to unobserved entries. Our approach is privacy-preserving, scalable, and readily applicable to downstream tasks such as classification and clustering. Extensive experiments on over 15 benchmark datasets demonstrate that HI-PMK consistently outperforms traditional imputation-based pipelines and kernel methods across a wide range of missing data settings. Code is available at: https://github.com/echoid/Incomplete-Heter-Kernel
Acquiring ground truth labels for unlabelled data can be a costly procedure, since it often requires manual labour that is error-prone. Consequently, the available amount of labelled data is increasingly reduced due to the limitations of manual data labelling. It is possible to increase the amount of labelled data samples by performing automated labelling or crowd-sourcing the annotation procedure. However, they often introduce noise or uncertainty in the labelset, that leads to decreased performance of supervised deep learning methods. On the other hand, weak supervision methods remain robust…
MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT
Hong Kong University of Science and Technology（香港科技大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；U.S. Army DEVCOM Army Research Laboratory（美国陆军DEVCOM陆军研究实验室）
FSRF: Factorization-guided Semantic Recovery for Incomplete Multimodal Sentiment Analysis
School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）；School of Advanced Manufacturing Engineering, Hefei University（合肥大学先进制造工程学院）；College of Global Talents, BITZH, Beijing Institute of Technology（北京理工大学珠海学院全球人才学院）；Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）
College of Computer Science & Technology, Nanjing University of Aeronautics & Astronautics（南京航空航天大学计算机科学与技术学院）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术与产业化协同创新中心）
Real data are often with multiple modalities or from multiple heterogeneous sources, thus forming so-called multi-view data, which receives more and more attentions in machine learning. Multi-view clustering (MVC) becomes its important paradigm. In real-world applications, some views often suffer from instances missing. Clustering on such multi-view datasets is called incomplete multi-view clustering (IMC) and quite challenging. To date, though many approaches have been developed, most of them are offline and have high computational and memory costs especially for large scale datasets. To addr…
Tianjin University（天津大学）；Automotive Data of China (Tianjin) Co., Ltd（中汽数据（天津）有限公司）
Multi-view learning has progressed rapidly in recent years. Although many previous studies assume that each instance appears in all views, it is common in real-world applications for instances to be missing from some views, resulting in incomplete multi-view data. To tackle this problem, we propose a novel Latent Heterogeneous Graph Network (LHGN) for incomplete multi-view learning, which aims to use multiple incomplete views as fully as possible in a flexible manner. By learning a unified latent representation, a trade-off between consistency and complementarity among different views is impli…
Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks
Guangdong University of Foreign Studies（广东外语外贸大学）；School of Engineering, Westlake University（西湖大学工程学院）；East China University of Science and Technology（华东理工大学）
Alan Turing Institute（艾伦·图灵研究所）；University Carlos III in Madrid（马德里卡洛斯三世大学）；University of Cambridge（剑桥大学）；Uber AI Labs（优步人工智能实验室）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data. However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomplete (with missing data at random), which is indeed common in real-world applications. In this paper, we propose a general framework to design VAEs suitable for fitting incomplete heterogenous data. The proposed HI-VAE includes likelihood models for real-valued, positive real val…
SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data
Technical University of Munich (TUM)（慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；School of Medicine and Health Technical University of Munich (TUM)（医学与健康学院慕尼黑技术大学）；Department of Radiation Oncology TUM University Hospital（放射肿瘤科TUM大学医院）；Chair for AI in Healthcare and Medicine Technical University of Munich (TUM)（医疗与医学人工智能系慕尼黑技术大学）；Department of Pathology School of Medicine Department of Biomedical Engineering School of Engineering University of California Irvine（病理学系医学院生物医学工程系工程学院加州大学伊文斯顿分校）
Politecnico di Milano（米兰理工大学）；Technische Universität Dresden（德累斯顿工业大学）
Survival analysis studies time-modeling techniques for an event of interest occurring for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, the data needed to train survival models are often distributed, incomplete, censored, and confidential. In this context, federated learning can be exploited to tremendously improve the quality of the models trained on distributed data while preserving user privacy. However, federated survival analysis is still in its early development, and there is no common benchmarking dataset to test …
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；JD AI Lab（京东人工智能实验室）；Ping An Technology（平安科技）
Federated learning involves training machine learning models over devices or data silos, such as edge processors or data warehouses, while keeping the data local. Training in heterogeneous and potentially massive networks introduces bias into the system, which is originated from the non-IID data and the low participation rate in reality. In this paper, we propose Elastic Federated Learning (EFL), an unbiased algorithm to tackle the heterogeneity in the system, which makes the most informative parameters less volatile during training, and utilizes the incomplete local updates. It is an efficien…
Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University（山东大学-南洋理工大学人工智能联合研究中心,山东大学）；College of Computer and Information Sciences, Southwest University（西南大学计算机与信息科学学院）；School of Software, Shandong University（山东大学软件学院）；CEMSE, King Abdullah University of Science and Technology（计算机、工程与数学科学学院,沙特阿拉伯国王阿卜杜拉科技大学）；Department of Computer Science, George Mason University（乔治梅森大学计算机科学系）
Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering. Most previous works search for only one optimal clustering based on the predefined clustering criterion, but devising such a criterion that captures what users need is difficult. Due to the multiplicity of multi-view data, we can have meaningful alternative clusterings. In addition, the incomplete multi-view data problem is ubiquitous in real world but has not been studied for multiple clusterings. To address these issues, we introduce a deep incomplete multi-view multiple clusterings …
Robust Multi-Omics Integration from Incomplete Modalities Significantly Improves Prediction of Alzheimer's Disease
LG AI Research, Seoul, Republic of Korea（LG AI研究院，韩国首尔）；Department of Chemistry, Seoul National University, Seoul, Republic of Korea（首尔国立大学化学系）；The Jackson Laboratory, Bar Harbor, Maine, USA（杰克逊实验室，缅因州巴尔 Harbor）
Tsinghua University（清华大学）；University of Connecticut（康涅狄格大学）；University of Michigan（密歇根大学）；Cornell University（康奈尔大学）
Finite Mixture Regression (FMR) refers to the mixture modeling scheme which learns multiple regression models from the training data set. Each of them is in charge of a subset. FMR is an effective scheme for handling sample heterogeneity, where a single regression model is not enough for capturing the complexities of the conditional distribution of the observed samples given the features. In this paper, we propose an FMR model that 1) finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously, 2) achieves shared feature selection among tasks and cluster comp…
Learning from Anonymized and Incomplete Tabular Data
Leipzig University \& ScaDS.AI Leipzig Germany；The Australian National University Canberra Australia；Leipzig University \& ScaDS.AI；The Australian National University
Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data
School of Computer Science and Engineering, Tianjin University of Technology（天津理工大学计算机科学与工程学院）；Technical University of Denmark（丹麦技术大学）；Aalborg University（奥尔堡大学）
TCDiff: Triplex Cascaded Diffusion for High-fidelity Multimodal EHRs Generation with Incomplete Clinical Data
School of Computer Science, Peking University（北京大学计算机科学学院）；School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）；National Engineering Research Center for Software Engineering, Peking University（软件工程国家工程研究中心）；Institute of Software, Chinese Academy of Science（中国科学院软件研究所）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；Institute of Basic Theory of Chinese Medicine, China Academy of Chinese Medical Sciences（中国中医科学院中医基础理论研究所）
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Data from multi-modality provide complementary information in clinical prediction, but missing data in clinical cohorts limits the number of subjects in multi-modal learning context. Multi-modal missing imputation is challenging with existing methods when 1) the missing data span across heterogeneous modalities (e.g., image vs. non-image); or 2) one modality is largely missing. In this paper, we address imputation of missing data by modeling the joint distribution of multi-modal data. Motivated by partial bidirectional generative adversarial net (PBiGAN), we propose a new Conditional PBiGAN (C…
IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities
School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；School of Information Science and Technology, Guangdong University of Foreign Studies（广东外语外贸大学信息科学与技术学院）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；School of Computer Science and Engineering, Huizhou University（惠州大学计算机科学与工程学院）
Integrate Any Omics: Towards genome-wide data integration for patient stratification
Peter Munk Cardiac Centre, University Health Network（彼得·蒙克心脏中心，大学健康网络）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Department of Molecular Genetics, University of Toronto（多伦多大学分子遗传学系）；Princess Margaret Cancer Centre, University Health Network（玛格丽特公主癌症中心，大学健康网络）；Department of Medical Biophysics, University of Toronto（多伦多大学医学生物物理学系）；Genetics and Genome Biology, the Hospital for Sick Children（病童医院遗传学与基因组生物学）
Extension OL-MDISF: Online Learning from Mix-Typed, Drifted, and Incomplete Streaming Features
College of Cyber Security, Jinan University, Guangzhou, Guangdong（广州大学网络安全学院）；College of Computer and Information Science, Southwest University, Chongqing（西南大学计算机与信息科学学院）；Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui（大数据知识工程重点实验室（教育部））
Wuhan University（武汉大学）；German Aerospace Center (DLR)（德国航空航天中心）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（亥姆霍兹德累斯顿罗森多夫研究中心）；Helmholtz Institute Freiberg for Resource Technology (HIF)（亥姆霍兹弗赖贝格资源技术研究所）；Technical University of Munich (TUM)（慕尼黑工业大学）
This paper proposes a novel framework for fusing multi-temporal, multispectral satellite images and OpenStreetMap (OSM) data for the classification of local climate zones (LCZs). Feature stacking is the most commonly-used method of data fusion but does not consider the heterogeneity of multimodal optical images and OSM data, which becomes its main drawback. The proposed framework processes two data sources separately and then combines them at the model level through two fusion models (the landuse fusion model and building fusion model), which aim to fuse optical images with landuse and buildin…
Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts
Platform of Bioinformatics（生物信息平台）；Lausanne University Hospital（洛桑大学医院）；Faculty of Biology and Medicine（生物学与医学学院）；University of Lausanne（洛桑大学）；Leenaards Memory Centre（Leenaards记忆中心）；Department of Clinical Neurosciences（临床神经科学系）
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…
Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with `Big Data' analytics. However, increasingly noisy, heterogeneous, and incomplete datasets as well as the need for {\em real-time} processing of streaming data pose major challenges to this end. In this context, the present paper permeates benefits from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling latent (possibly multi-way) structure from \emph{incomplete streaming} data. For low-rank matr…
