Multi-view collaborative locally adaptive clustering with Minkowski metric

Recently, many heterogeneous but related views of data have been generated in a number of applications. Different views may represent distinct aspects of the same data, which often have the same or consensus cluster structure. Discovering cluster structure in multi-view data has become a hot research topic and significant progress has been made in multi-view clustering. However, it remains a challenging issue to exploit the diversity within each view and investigate the relationship across multiple views simultaneously. To address the above issues, in this paper, we extend locally adaptive clustering into a multi-view framework with Minkowski metric and propose a novel approach termed multi-view collaborative locally adaptive clustering with Minkowski metric (MV-CoMLAC). Different from the existing multi-view subspace clustering methods, the proposed approach is capable of simultaneously taking into account the subspace diversity within each view as well as the knowledge across different views. A collaborative strategy is designed to exploit the complementary information from different low-dimensional subspaces. Furthermore, Minkowski metric is utilized to take into account the influence of the L-p distance (p ≥ 0), making our method adaptive to different application tasks. Extensive experiments have been conducted on several multi-view datasets, which demonstrate the superiority of our approach over the existing multi-view clustering methods.     

Kernelized multi-view subspace clustering via auto-weighted graph learning

Multi-view subspace clustering has been an important and powerful tool for partitioning multi-view data, especially multi-view high-dimensional data. Despite great success, most of the existing multi-view subspace clustering methods still suffer from three limitations. First, they often recover the subspace structure in the original space, which can not guarantee the robustness when handling multi-view data with nonlinear structure. Second, these methods mostly regard subspace clustering and affinity matrix learning as two independent steps, which may not well discover the latent relationships among data samples. Third, many of them ignore the different importance of multiple views, whose performance may be badly affected by the low-quality views in multi-view data. To overcome these three limitations, this paper develops a novel subspace clustering method for multi-view data, termed Kernelized Multi-view Subspace Clustering via Auto-weighted Graph Learning (KMSC-AGL). Specifically, the proposed method implicitly maps the multi-view data from linear space into nonlinear space via kernel-induced functions, so as to exploit the nonlinear structure hidden in data. Furthermore, our method aims to enhance the clustering performance by learning a set of view-specific representations and their affinity matrix in a general framework. By integrating the view weighting strategy into this framework, our method can automatically assign the weights to different views, while learning an optimal affinity matrix that is well-adapted to the subsequent spectral clustering. Extensive experiments are conducted on a variety of multi-view data sets, which have demonstrated the superiority of the proposed method.

     

多样性引导的深度多视图聚类算法

多视图聚类旨在从不同视图的多样性信息中, 学习到更加全面和准确的共识表示, 以提高模型的聚类性能. 目前大部分多视图聚类算法采用希尔伯特-施密特独立性准则(HSIC)或自适应加权方法从全局考虑各视图的多样性, 忽略了各视图样本之间的局部多样性信息学习. 针对上述问题, 提出了多样性引导的深度多视图聚类算法. 首先, 提出了融合多头自注意力机制的软聚类模块, 多头自注意力机制用来学习全局多样性, 软聚类模糊C均值算法用来学习局部多样性; 其次, 在深度图自编码器网络结构中引入软聚类模块, 以达到多样性信息引导潜在表示生成的目的; 然后, 将得到的各视图潜在表示进行加权融合得到共识表示, 并采用谱聚类算法对共识表示进行聚类; 最后, 在3个常用数据集上进行了对比实验和消融实验. 实验结果表明, 提出的聚类算法具有良好的聚类效果, 以及提出的多样性信息学习模块可以有效提高算法聚类性能.     

Deep cross-view autoencoder network for multi-view learning

In many real-world applications, an increasing number of objects can be collected at varying viewpoints or by different sensors, which brings in the urgent demand for recognizing objects from distinct heterogeneous views. Although significant progress has been achieved recently, heterogeneous recognition (cross-view recognition) in multi-view learning is still challenging due to the complex correlations among views. Multi-view subspace learning is an effective solution, which attempts to obtain a common representation from downstream computations. Most previous methods are based on the idea of maximal correlation after feature extraction to establish the relationship among different views in a two-step manner, thus leading to performance deterioration. To overcome this drawback, in this paper, we propose a deep cross-view autoencoder network (DCVAE) that extracts the features of different views and establishes the correlation between views in one step to simultaneously handle view-specific, view-correlation, and consistency in a joint manner. Specifically, DCVAE contains self-reconstruction, newly designed cross-view reconstruction, and consistency constraint modules. Self-reconstruction ensures the view-specific, cross-view reconstruction transfers the information from one view to another view, and consistency constraint makes the representation of different views more consistent. The proposed model suffices to discover the complex correlation embedded in multi-view data and to integrate heterogeneous views into a latent common representation subspace. Furthermore, the 2D embeddings of the learned common representation subspace demonstrate the consistency constraint is valid and cross-view classification experiments verify the superior performance of DCVAE in the two-view scenario.

     

Diverse Deep Matrix Factorization With Hypergraph Regularization for Multi-View Data Representation

Deep matrix factorization(DMF) has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data(MDR). However, existing multiview DMF methods mainly explore the consistency of multi-view data, while neglecting the diversity among different views as well as the high-order relationships of data, resulting in the loss of valuable complementary information. In this paper, we design a hypergraph regularized diverse deep matrix factorization(HDDMF) model f...     

基于深度自编码的多视图子空间聚类网络

传统子空间浅层聚类模型对于多视图和非线性数据的聚类性能不佳。为此,提出一种基于深度自编码器的多视图子空间聚类网络模型,通过在深度自编码器中引入子空间聚类中的“自我表示”特性以及加权稀疏表示,提升了多视图子空间聚类算法的学习能力。推导的深度自编码多视图子空间聚类算法能够聚类具有复杂结构的数据点。通过多视图数据集验证了提出算法的有效性。结果表明,该方法能够有效地挖掘数据固有的多样性聚类结构,并利用多个视图之间互补信息,在性能上与现有方法相比有较大的提升。     

基于图信息的自监督多视角子空间聚类

多视角子空间聚类方法通常用于处理高维度、复杂结构的数据.现有的大多数多视角子空间聚类方法通过挖掘潜在图信息进行数据分析与处理,但缺乏对潜在子空间表示的监督过程.针对这一问题,本文提出一种新的多视角子空间聚类方法,即基于图信息的自监督多视角子空间聚类(SMSC).它将谱聚类与子空间表示相结合形成统一的深度学习框架.SMS...     

基于格拉斯曼流形子空间融合的多视图聚类

现有的多视图聚类算法大多假设多视图数据点之间为线性关系,且在学习过程中无法保留原始特征空间的局部性;而在欧氏空间中进行子空间融合又过于单调,无法将学习到的子空间表示对齐。针对以上问题,提出了基于格拉斯曼流形融合子空间的多视图聚类算法。首先,将核技巧和局部流形结构学习结合以得到不同视图的子空间表示;然后,在格拉斯曼流形上融合这些子空间表示以得到一致性亲和矩阵;最后,对一致性亲和矩阵执行谱聚类来得到最终的聚类结果,并利用交替方向乘子法（ADMM）来优化所提模型。与核多视图低秩稀疏子空间聚类（KMLRSSC）算法相比,所提算法的聚类精度在MSRCV1、Prokaryotic、Not-Hill数据集上分别提高了20.83个百分点、9.47个百分点和7.33个百分点。实验结果验证了基于格拉斯曼流形融合子空间的多视图聚类算法的有效性和良好性能。     

Frobenius norm-regularized robust graph learning for multi-view subspace clustering

Graph learning methods have been widely used for multi-view clustering. However, such methods have the following challenges: (1) they usually perform simple fusion of fixed similarity graph matrices, ignoring its essential structure. (2) they are sensitive to noise and outliers because they usually learn the similarity matrix from the raw features. To solve these problems, we propose a novel multi-view subspace clustering method named Frobenius norm-regularized robust graph learning (RGL), which inherits desirable advantages (noise robustness and local information preservation) from the subspace clustering and manifold learning. Specifically, RGL uses Frobenius norm constraint and adjacency similarity learning to simultaneously explore the global information and local similarity of views. Furthermore, the l_2,1 norm is imposed on the error matrix to remove the disturbance of noise and outliers. An effectively iterative algorithm is designed to solve the RGL model by the alternation direction method of multipliers. Extensive experiments on nine benchmark databases show the clear advantage of the proposed method over fifteen state-of-the-art clustering methods.

     

面向不完整多视图聚类的深度互信息最大化方法

多视图聚类是无监督学习领域研究热点之一,近年来涌现出许多优秀的多视图聚类工作,但其中大多数方法均假设各视图是完整的,然而真实场景下数据收集过程极容易发生缺失,造成部分视图不完整。同时,很多方法采取传统机器学习方法（即浅层模型）对数据进行特征学习,这导致模型难以挖掘高维数据内的复杂信息。针对以上问题,本文提出一种面向不完整多视图聚类的深度互信息最大化方法。首先利用深度自编码器挖掘各视图深层次的隐含特征,并通过最大化潜在表示间的互信息来学习各视图间的一致性知识。然后,对于不完整视图中的缺失数据,利用多视图的公共潜在表示进行补全。此外,本文采用一种自步学习策略对网络进行微调,从易到难地学习数据集中的样本,得到更加宜于聚类的特征表示。最后,在多个真实数据集上进行实验,验证了本文方法的有效性。     

基于深度图正则化矩阵分解的多视图聚类算法

针对现实社会中由多种表示或视图组成的多视图数据广泛存在的问题,深度矩阵分解模型因其能够挖掘数据的层次信息而备受关注,但该模型忽略了数据的几何结构信息.为解决以上问题,本文提出基于深度图正则化矩阵分解的多视图聚类算法,通过获取每个视图的局部结构信息和全局结构信息在逐层分解中加入两个图正则化限制,保护多视图数据的几何结构信...     

自适应加权低秩约束的多视图子空间聚类算法

多视图聚类旨在综合利用视图数据中的一致信息和互补信息实现对数据的划分,但各视图表征数据的能力参差不齐,甚至有的视图可能含有大量的冗余和噪声信息,不仅不能带来多样的信息,反而影响聚类性能.本文提出了自适应加权的低秩约束的多视图子空间聚类算法,通过自适应学习的方式给各视图赋予不同权重来构造各视图共享的潜在一致低秩矩阵.并且提出了有效的可迭代优化算法对模型进行优化.在5个公开数据集上的实验结果表明所提算法的有效性.     

生成式不完整多视图数据聚类

基于自表示子空间聚类的多视图聚类引起越来越多的关注. 大多数现有算法假设每个样本的所有视图都可获得, 然而在实际应用中, 由于各种因素, 可能会导致某些视图缺失. 为了对视图不完整数据进行聚类, 本文提出了一种在统一框架下同时执行缺失视图补全和多视图子空间聚类的方法. 具体地, 缺失视图是由已观测视图数据约束的隐表示生成的. 此外, 多秩张量应用于挖掘不同视图之间的高阶相关性. 这样通过隐表示和高阶张量同时挖掘了不同视图以及所有样本(即使是不完整视图样本)之间的相关性. 本文使用增广拉格朗日交替方向最小化(AL-ADM)方法求解优化问题. 在真实数据集上的实验结果表明, 我们的方法优于最新的多视图聚类算法, 具有更好的聚类准确度和鲁棒性.     

大规模多视图数据的自降维K-means算法

为了提升传统多视图K-means算法在高维数据中的聚类性能,提出了一种鲁棒性大规模多视图数据的自降维K-means算法RMSKMC（robust multi-view subKmeans clustering）,通过寻找单个视图上的最优子空间实现高维数据的自降维,利用非负矩阵分解（NMF）对损失函数进行重构,使不同视图共享相同的聚类指示矩阵从而实现多视图信息互补,完成大规模多视图数据的聚类。实验结果表明,在大规模多视图数据集上,该算法比其他多视图聚类算法资源消耗更小,并且能够进行更为准确的聚类。     

利用正则化矩阵分解技术的多视图聚类方法

为了解决具有多种特征属性的多媒体数据（多视图数据）挖掘问题,在非负矩阵分解（NMF）算法的基础上,提出了一种多视图正则化矩阵分解算法（MRMF）,该算法使用了多元非负矩阵分解技术,同时使用[L2,1]范数描述矩阵分解的损失函数,并采用多视图流形正则化对矩阵分解进行正则化约束。与现有的一些数据聚类或多视图聚类算法相比,提出的MRMF算法不易受到原始数据中噪声的影响,而且能够充分考虑到不同视图在聚类中所具有不同权重的问题,能够对多视图数据进行较为准确的聚类。MRMF算法的有效性在一些经典的公开数据集上进行了验证,并取得了较好的聚类精度。     

一致性引导的自适应加权多视图聚类

随着获取多模态或多视图数据的日益容易,多视图聚类研究受到广泛关注.然而,很多方法直接从原始数据中学习邻接矩阵,忽视了数据中噪声的影响.此外,还有一些方法将各个视图同等对待,而实际上各视图在聚类过程中所发挥的作用是不同的.为解决上述问题,提出了一种基于Markov链的聚类算法,名为一致性引导的自适应加权多视图聚类(consensus guided auto-weighted multi-view clustering, CAMC).首先为每个视图构造转移概率矩阵;然后,以自适应加权的方式获得一致性转移概率矩阵,并对一致性转移概率矩阵的拉普拉斯矩阵进行了秩约束,确保拉普拉斯图中连通分量的数目正好等于簇的数目.此外,基于交替方向乘子法(alternating direction method of multipliers, ADMM)优化策略对问题进行求解.在1个人造数据集和7个真实数据集上的实验结果证明了该算法的有效性,其聚类性能优于现有的8种基准算法.     

双加权多视角子空间聚类算法

多视角子空间聚类方法为高维多视角数据的聚类问题提供了大量的解决方案.但是现有的子空间方法仍不能很好地解决以下两个问题:(1)如何利用不同视角的差异性进行学习获得一个优质的共享系数矩阵;(2)如何增强共享系数矩阵的低秩性.针对以上问题,提出了一种有效的双加权多视角子空间聚类算法.该算法首先通过子空间自表达学习到每个视角的...     

基于一致图学习的鲁棒多视图子空间聚类

针对多视图数据分析易受原始数据集噪声干扰,以及需要额外的步骤计算聚类结果的问题,提出一种基于一致图学习的鲁棒多视图子空间聚类（RMCGL）算法。首先,在各个视图下学习数据在子空间中的潜在鲁棒表示,并基于该表示得到各视图的相似度矩阵。随后,基于得到的多个相似度矩阵学习一个统一的相似度图。最后,通过对相似度图对应的拉普拉斯矩阵添加秩约束,确保得到的相似度图具有最优的聚类结构,并可直接得到最终的聚类结果。该过程在一个统一的优化框架中完成,能同时学习潜在鲁棒表示、相似度矩阵和一致图。RMCGL算法的聚类精度（ACC）在BBC、100leaves和MSRC数据集上比基于图的多视图聚类（GMC）算法分别提升了3.36个百分点、5.82个百分点和5.71个百分点。实验结果表明,该算法具有良好的聚类效果。     

特征直连与结构化约束的多视图子空间聚类

多视图子空间聚类作为处理多视图数据的聚类算法,其目的在于学习到一个共识的子空间后用于聚类。但是,现存的多视图子空间聚类算法只是将目标放在了原有的多个视图上,忽略了通过特征直连得到的数据。提出的FSMC算法使原有的多个视图与特征直连视图相互学习,通过误差重构和结构化约束子空间得到一个更加合适的子空间表示,同时还考虑了多视图与特征直连视图的权重关系。最后,在4个基准数据集上进行实验,验证了算法的有效性。     

基于多阶近邻融合的不完整多视图聚类算法

在实际应用中,聚类多视图数据是一项重要的数据挖掘任务.样本缺失所导致的多视图不完整给聚类任务带来了巨大的挑战.大部分已有的不完整多视图聚类方法主要基于浅层图结构信息,易受到噪声及缺失数据的影响,且难以准确刻画并兼容所有视图的潜在结构,从而降低了聚类性能.为此,提出了一种更为鲁棒和灵活的基于多阶近邻扩散融合的不完整多视图...