Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Guangdong University of Technology（广东工业大学）；University of Macau（澳门大学）；Hefei University of Technology（合肥工业大学）
Jie Wen, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu, Zhao Zhang, Jinxing Li
备注：
Accepted by IEEE Transactions on Systems, Man, and Cybernetics: Systems (2022)
Conventional multi-view clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multi-view clustering methods. Clustering on such incomplete multi-view data is referred to as incomplete multi-view clustering. In view of the promising application prospects, the research of incomplete multi-view clustering has noticeable advances in recent years. However, there is no survey to summarize the current progresses and point out the future research directions. To this end, we review the recent studies of incomplete multi-view clustering. Importantly, we provide some frameworks to unify the corresponding incomplete multi-view clustering methods, and make an in-depth comparative analysis for some representative methods from theoretical and experimental perspectives. Finally, some open problems in the incomplete multi-view clustering field are offered for researchers.
Multiple datasets containing different types of features may be available for a given task. For instance, users' profiles can be used to group users for recommendation systems. In addition, a model can also use users' historical behaviors and credit history to group users. Each dataset contains different information and suffices for learning. A number of clustering algorithms on multiple datasets were proposed during the past few years. These algorithms assume that at least one dataset is complete. So far as we know, all the previous methods will not be applicable if there is no complete datas…
Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence. However, this algorithm suffers from incomplete data, where some samples have missed some of their attributes. To solve this problem, we mainly apply MM principles to restore the symmetry of the data, so that K-means could work well. We give the pseudo-code of the algorithm and use the standard datasets for experimental verification. The source code for the experiments is publicly available in the following link: \url{https://github.com/AliBeikmohammadi/MM-Opt…
Kernel-based clustering algorithm can identify and capture the non-linear structure in datasets, and thereby it can achieve better performance than linear clustering. However, computing and storing the entire kernel matrix occupy so large memory that it is difficult for kernel-based clustering to deal with large-scale datasets. In this paper, we employ incomplete Cholesky factorization to accelerate kernel clustering and save memory space. The key idea of the proposed kernel $k$-means clustering using incomplete Cholesky factorization is that we approximate the entire kernel matrix by the prod…
School of Computer, National University of Defense Technology（国防科技大学计算机学院）
Clustering is a fundamental task in the computer vision and machine learning community. Although various methods have been proposed, the performance of existing approaches drops dramatically when handling incomplete high-dimensional data (which is common in real world applications). To solve the problem, we propose a novel deep incomplete clustering method, named Deep Distribution-preserving Incomplete Clustering with Optimal Transport (DDIC-OT). To avoid insufficient sample utilization in existing methods limited by few fully-observed samples, we propose to measure distribution distance with …
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Shenzhen University（深圳大学）
In the era of big data, it is common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view clustering provides a natural way to generate clusters from such data. Since different views share some consistency and complementary information, previous works on multi-view clustering mainly focus on how to combine various numbers of views to improve clustering performance. However, in reality, each view may be incomplete, i.e., instances missing in the view. Furthermore, the size of data could be extremely huge. It is unrealistic to apply multi-…
Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat
School of Computer Science and Technology, Key Laboratory of Information Storage System Ministry of Education of China, Huazhong University of Science and Technology（计算机科学与技术学院，信息存储系统教育部重点实验室，华中科技大学）；Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology（大数据安全工程研究中心，网络安全学院，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）
Clustering Result Re-guided Incomplete Multi-view Spectral Clustering
College of Information Engineering, Shanghai Maritime University（上海 Maritime University 信息工程学院）；Department of Automation, Shanghai Jiao Tong University（上海 Jiao Tong University 自动化系）
In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete multi-view clustering, the view-missing problem increases the difficulty of learning common representations from different views. To address the challenge, we propose a novel incomplete multi-view clustering framework, which incorporates cross-view relation transfer and multi-view fusion learning. Specifically, based on the consistency existing in multi-view data, we devise a cross-view relation transfer-based completion module, which transfers known similar inter-instance relationships …
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 …
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…
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）
Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a comm…
Southwest Jiaotong University（西南交通大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Dalian University of Technology（大连理工大学）
Beyond existing multi-view clustering, this paper studies a more realistic clustering scenario, referred to as incomplete multi-view clustering, where a number of data instances are missing in certain views. To tackle this problem, we explore spectral perturbation theory. In this work, we show a strong link between perturbation risk bounds and incomplete multi-view clustering. That is, as the similarity matrix fed into spectral clustering is a quantity bounded in magnitude O(1), we transfer the missing problem from data to similarity and tailor a matrix completion method for incomplete similar…
CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network
Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳校区生物计算研究中心）；Shenzhen Key Laboratory of Visual Object Detection and Recognition（深圳市视觉目标检测与识别重点实验室）；Pengcheng Laboratory（鹏城实验室）；University of Macau（澳门大学）；Guangdong University of Technology（广东工业大学）；Inception Institute of Artificial Intelligence（inception人工智能研究院）
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）
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The University of Queensland（昆士兰大学）
Clustering with incomplete views is a challenge in multi-view clustering. In this paper, we provide a novel and simple method to address this issue. Specifically, the proposed method simultaneously exploits the local information of each view and the complementary information among views to learn the common latent representation for all samples, which can greatly improve the compactness and discriminability of the obtained representation. Compared with the conventional graph embedding methods, the proposed method does not introduce any extra regularization term and corresponding penalty paramet…
Mask-informed Deep Contrastive Incomplete Multi-view Clustering
Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Computer Science, China University of Geosciences（中国地质大学计算机学院）
In this paper, we study how to achieve two characteristics highly-expected by incomplete multi-view clustering (IMvC). Namely, i) instance commonality refers to that within-cluster instances should share a common pattern, and ii) view versatility refers to that cross-view samples should own view-specific patterns. To this end, we design a novel dual-stream model which employs a dual attention layer and a dual contrastive learning loss to learn view-specific prototypes and model the sample-prototype relationship. When the view is missed, our model performs data recovery using the prototypes in …
The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views. However, in real-world scenarios, samples of multi-view are partially available due to data corruption or sensor failure, which leads to incomplete multi-view clustering study (IMVC). Although several attempts have been proposed to address IMVC, they suffer from the following drawbacks: i) Existing methods mainly adopt cross-view contrastive learning forcing the representations of each sample across views to be exactly the same, which might ignore view discrepancy and flexibility i…
Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance
School of Computer, National University of Defense Technology（国防科技大学计算机学院）；Academy of Military Sciences（军事科学院）；Chinese PLA General Hospital（中国人民解放军总医院）
Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering
Nanjing University（南京大学）；Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs（农业农村部南京农业机械化研究所）；Nanjing University of Science and Technology（南京理工大学）
Eli Lilly and Company（礼来公司）；Iowa State University（爱荷华州立大学）
The $k$-means algorithm is arguably the most popular nonparametric clustering method but cannot generally be applied to datasets with incomplete records. The usual practice then is to either impute missing values under an assumed missing-completely-at-random mechanism or to ignore the incomplete records, and apply the algorithm on the resulting dataset. We develop an efficient version of the $k$-means algorithm that allows for clustering in the presence of incomplete records. Our extension is called $k_m$-means and reduces to the $k$-means algorithm when all records are complete. We also provi…
Georgia State University（佐治亚州立大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
This paper introduces {\em fusion subspace clustering}, a novel method to learn low-dimensional structures that approximate large scale yet highly incomplete data. The main idea is to assign each datum to a subspace of its own, and minimize the distance between the subspaces of all data, so that subspaces of the same cluster get {\em fused} together. Our method allows low, high, and even full-rank data; it directly accounts for noise, and its sample complexity approaches the information-theoretic limit. In addition, our approach provides a natural model selection {\em clusterpath}, and a direc…
V3H: View Variation and View Heredity for Incomplete Multi-view Clustering
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学大数据安全工程研究中心）；Department of Electrical and Computer Engineering, University of Florida（佛罗里达大学电子与计算机工程系）
Bryn Mawr College（布林莫尔学院）；University of Maryland, Baltimore County（马里兰大学巴尔的摩分校）；Lockheed Martin Advanced Technology Laboratories（洛克希德·马丁先进技术实验室）
Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other vi…
Modern inference and learning often hinge on identifying low-dimensional structures that approximate large scale data. Subspace clustering achieves this through a union of linear subspaces. However, in contemporary applications data is increasingly often incomplete, rendering standard (full-data) methods inapplicable. On the other hand, existing incomplete-data methods present major drawbacks, like lifting an already high-dimensional problem, or requiring a super polynomial number of samples. Motivated by this, we introduce a new subspace clustering algorithm inspired by fusion penalties. The …
Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
School of Computer Science and Technology, Harbin Institute of Technology, Weihai, China（哈尔滨工业大学威海学院）；School of Software, Shandong University, Jinan, China（山东大学软件学院）；Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, China（深圳视觉对象检测与识别重点实验室）
Missing Pattern Tree based Decision Grouping and Ensemble for Enhancing Pair Utilization in Deep Incomplete Multi-View Clustering
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Department of Computer Science and Engineering, Lehigh University（莱斯大学计算机科学与工程系）；Department of Computer Science, University of Illinois Chicago（伊利诺伊大学芝加哥分校计算机科学系）；School of Computer Science and Technology, Hainan University（海南大学计算机科学与技术学院）
Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance
College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；University of Technology, Sydney（悉尼科技大学）；Hefei University of Technology（合肥工业大学）
Global-Graph Guided and Local-Graph Weighted Contrastive Learning for Unified Clustering on Incomplete and Noise Multi-View Data
Guangxi Normal University（广西师范学院）；University of Electronic Science and Technology of China（电子科技大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Hainan University（海南大学）
Guangxi Normal University（广西师范大学）；Huazhong University of Science and Technology（华中科技大学）
Clustering attempts to partition data instances into several distinctive groups, while the similarities among data belonging to the common partition can be principally reserved. Furthermore, incomplete data frequently occurs in many realworld applications, and brings perverse influence on pattern analysis. As a consequence, the specific solutions to data imputation and handling are developed to conduct the missing values of data, and independent stage of knowledge exploitation is absorbed for information understanding. In this work, a novel approach to clustering of incomplete data, termed lea…
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）
Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering
School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University（北京交通大学交通数据挖掘与分析重点实验室）；School of Medicine, Zhejiang University（浙江大学医学院）
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
With the advancement of the data acquisition techniques, multi-view learning has become a hot topic. Some multi-view learning methods assume that the multi-view data is complete, which means that all instances are present, but this too ideal. Certain tensor-based methods for handing incomplete multi-view data have emerged and have achieved better result. However, there are still some problems, such as use of traditional tensor norm which makes the computation high and is not able to handle out-of-sample. To solve these two problems, we proposed a new incomplete multi view learning method. A ne…
Southwestern University of Finance and Economics（西南财经大学）；JD Intelligent Cities Research（京东智能城市研究院）；Minnan Normal University（闽南师范大学）；FernUniversität in Hagen（哈根函授大学）；Southwest Jiaotong University（西南交通大学）
Multi-view unsupervised feature selection has been proven to be efficient in reducing the dimensionality of multi-view unlabeled data with high dimensions. The previous methods assume all of the views are complete. However, in real applications, the multi-view data are often incomplete, i.e., some views of instances are missing, which will result in the failure of these methods. Besides, while the data arrive in form of streams, these existing methods will suffer the issues of high storage cost and expensive computation time. To address these issues, we propose an Incremental Incomplete Multi-…
Royal Holloway University of London（伦敦大学皇家霍洛威学院）；University of Bergen（卑尔根大学）；IIT Hyderabad（印度理工学院海得拉巴分校）
We consider a generalization of the fundamental $k$-means clustering for data with incomplete or corrupted entries. When data objects are represented by points in $\mathbb{R}^d$, a data point is said to be incomplete when some of its entries are missing or unspecified. An incomplete data point with at most $Δ$ unspecified entries corresponds to an axis-parallel affine subspace of dimension at most $Δ$, called a $Δ$-point. Thus we seek a partition of $n$ input $Δ$-points into $k$ clusters minimizing the $k$-means objective. For $Δ=0$, when all coordinates of each point are specified, this is th…
In real-world applications, not all instances in multi-view data are fully represented. To deal with incomplete data, Incomplete Multi-view Learning (IML) rises. In this paper, we propose the Joint Embedding Learning and Low-Rank Approximation (JELLA) framework for IML. The JELLA framework approximates the incomplete data by a set of low-rank matrices and learns a full and common embedding by linear transformation. Several existing IML methods can be unified as special cases of the framework. More interestingly, some linear transformation based complete multi-view methods can be adapted to IML…
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…
Southwestern University of Finance and Economics（西南财经大学）；JD Intelligent Cities Research（京东智能城市研究院）；JD Intelligent Cities Business Unit（京东智能城市事业部）；University of Central Florida（中佛罗里达大学）；Southwest Jiaotong University（西南交通大学）
Multi-view unsupervised feature selection (MUFS) has been demonstrated as an effective technique to reduce the dimensionality of multi-view unlabeled data. The existing methods assume that all of views are complete. However, multi-view data are usually incomplete, i.e., a part of instances are presented on some views but not all views. Besides, learning the complete similarity graph, as an important promising technology in existing MUFS methods, cannot achieve due to the missing views. In this paper, we propose a complementary and consensus learning-based incomplete multi-view unsupervised fea…
