Co-clustering: A Survey of the Main Methods, Recent Trends, and Open Problems
Since its early formulations, co-clustering has gained popularity and interest both within and
outside the machine learning community as a powerful learning paradigm for clustering high …
outside the machine learning community as a powerful learning paradigm for clustering high …
An optimal statistical and computational framework for generalized tensor estimation
An optimal statistical and computational framework for generalized tensor estimation Page 1 The
Annals of Statistics 2022, Vol. 50, No. 1, 1–29 https://doi.org/10.1214/21-AOS2061 © Institute of …
Annals of Statistics 2022, Vol. 50, No. 1, 1–29 https://doi.org/10.1214/21-AOS2061 © Institute of …
Exact clustering in tensor block model: Statistical optimality and computational limit
High-order clustering aims to identify heterogeneous substructures in multiway datasets that
arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex …
arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex …
Convex clustering: Model, theoretical guarantee and efficient algorithm
Clustering is a fundamental problem in unsupervised learning. Popular methods like K-
means, may suffer from poor performance as they are prone to get stuck in its local minima …
means, may suffer from poor performance as they are prone to get stuck in its local minima …
A doubly enhanced em algorithm for model-based tensor clustering
Modern scientific studies often collect datasets in the form of tensors. These datasets call for
innovative statistical analysis methods. In particular, there is a pressing need for tensor …
innovative statistical analysis methods. In particular, there is a pressing need for tensor …
Multiway clustering via tensor block models
M Wang, Y Zeng - Advances in neural information …, 2019 - proceedings.neurips.cc
We consider the problem of identifying multiway block structure from a large noisy tensor.
Such problems arise frequently in applications such as genomics, recommendation system …
Such problems arise frequently in applications such as genomics, recommendation system …
A review of convex clustering from multiple perspectives: models, optimizations, statistical properties, applications, and connections
Q Feng, CLP Chen, L Liu - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
Traditional partition-based clustering is very sensitive to the initialized centroids, which are
easily stuck in the local minimum due to their nonconvex objectives. To this end, convex …
easily stuck in the local minimum due to their nonconvex objectives. To this end, convex …
Multiway graph signal processing on tensors: Integrative analysis of irregular geometries
Graph signal processing (GSP) is an important methodology for studying data residing on
irregular structures. Because acquired data are increasingly taking the form of multiway …
irregular structures. Because acquired data are increasingly taking the form of multiway …
Inference for low-rank tensors—no need to debias
Inference for low-rank tensors-no need to debias Page 1 The Annals of Statistics 2022, Vol. 50,
No. 2, 1220–1245 https://doi.org/10.1214/21-AOS2146 © Institute of Mathematical Statistics …
No. 2, 1220–1245 https://doi.org/10.1214/21-AOS2146 © Institute of Mathematical Statistics …
Integrative generalized convex clustering optimization and feature selection for mixed multi-view data
In mixed multi-view data, multiple sets of diverse features are measured on the same set of
samples. By integrating all available data sources, we seek to discover common group …
samples. By integrating all available data sources, we seek to discover common group …