利用张量核函数减少深度聚类中的目标函数不匹配

Bayesian Distance Clustering

Model-based clustering is widely-used in a variety of application areas. However, fundamental concerns remain about robustness. In particular, results can be sensitive to the choice of kernel representing the within-cluster data density. Leveraging on properties of pairwise differences between data points, we propose a class of Bayesian distance clustering methods, which rely on modeling the likelihood of the pairwise distances in place of the original data. Although some information in the data is discarded, we gain substantial robustness to modeling assumptions. The proposed approach represe…

面向鲁棒非线性子空间聚类的核学习方法

Towards Robust Nonlinear Subspace Clustering: A Kernel Learning Approach

本文提出DKLM，一种数据驱动的核学习非线性子空间聚类方法，通过自适应核学习与块对角亲和矩阵优化，提升聚类鲁棒性并在实验中验证其有效性。

Twin Learning for Similarity and Clustering: A Unified Kernel Approach

Many similarity-based clustering methods work in two separate steps including similarity matrix computation and subsequent spectral clustering. However, similarity measurement is challenging because it is usually impacted by many factors, e.g., the choice of similarity metric, neighborhood size, scale of data, noise and outliers. Thus the learned similarity matrix is often not suitable, let alone optimal, for the subsequent clustering. In addition, nonlinear similarity often exists in many real world data which, however, has not been effectively considered by most existing methods. To tackle t…

Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data

Datasets with a mixture of numerical and categorical attributes are routinely encountered in many application domains. In this work we examine an approach to clustering such datasets using homogeneity analysis. Homogeneity analysis determines a euclidean representation of the data. This can be analyzed by leveraging the large body of tools and techniques for data with a euclidean representation. Experiments conducted as part of this study suggest that this approach can be useful in the analysis and exploration of big datasets with a mixture of numerical and categorical attributes.

Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case

This paper introduces a clustering framework for networks with nodes annotated with time-series data. The framework addresses all types of network-clustering problems: State clustering, node clustering within states (a.k.a. topology identification or community detection), and even subnetwork-state-sequence identification/tracking. Via a bottom-up approach, features are first extracted from the raw nodal time-series data by kernel autoregressive-moving-average modeling to reveal non-linear dependencies and low-rank representations, and then mapped onto the Grassmann manifold (Grassmannian). All…

A Deep Variational Approach to Clustering Survival Data

In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference. In contrast to previous work, our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and censored survival times. We compare our model to the related work on clustering and mixture models for survival data in comprehensive experiments on a wide range of sy…

Discriminative Clustering for Robust Unsupervised Domain Adaptation

Unsupervised domain adaptation seeks to learn an invariant and discriminative representation for an unlabeled target domain by leveraging the information of a labeled source dataset. We propose to improve the discriminative ability of the target domain representation by simultaneously learning tightly clustered target representations while encouraging that each cluster is assigned to a unique and different class from the source. This strategy alleviates the effects of negative transfer when combined with adversarial domain matching between source and target representations. Our approach is rob…

多视图关系嵌入与聚类的Gromov-Wasserstein方法

Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering

本文提出Bary-GWMDS和Mean-GWMDS-C方法，通过直接操作距离矩阵学习共识嵌入，保留共享关系结构，并在合成和现实数据集上验证了稳定且几何意义显著的嵌入效果。