Multiple Sample Clustering
The clustering algorithms that view each object data as a single sample drawn from a certain distribution, Gaussian distribution, for example, has been a hot topic for decades. Many clustering algorithms: such as k-means and spectral clustering are proposed based on the single sample assumption. However, in real life, each input object can usually be the multiple samples drawn from a certain hidden distribution. The traditional clustering algorithms cannot handle such a situation. This calls for the multiple sample clustering algorithm. But the traditional multiple sample clustering algorithms can only handle scalar samples or samples from Gaussian distribution. This constrains the application field of multiple sample clustering algorithms. In this paper, we purpose a general framework for multiple sample clustering. Various algorithms can be generated by this framework. We apply two specific cases of this framework: Wasserstein distance version and Bhattacharyya distance version on both synthetic data and stock price data. The simulation results show that the sufficient statistic can greatly improve the clustering accuracy and stability.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering
Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent years. However, most existing deep clus…
ClusteringContrastive LearningDeep ClusteringMulti-View Multiple Clusterings using Deep Matrix Factorization
Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can only output a single clustering of the dat…
ClusteringDiversityA Novel Multi-clustering Method for Hierarchical Clusterings, Based on Boosting
Bagging and boosting are proved to be the best methods of building multiple classifiers in classification combination problems. In the area of "flat clustering" problems, it is also recognized that multi-clustering metho…
ClusteringCube Sampled K-Prototype Clustering for Featured Data
Clustering large amount of data is becoming increasingly important in the current times. Due to the large sizes of data, clustering algorithm often take too much time. Sampling this data before clustering is commonly use…
ClusteringSuspicion-Free Adversarial Attacks on Clustering Algorithms
Clustering algorithms are used in a large number of applications and play an important role in modern machine learning-- yet, adversarial attacks on clustering algorithms seem to be broadly overlooked unlike supervised l…
Adversarial AttackClustering