Recently deep learning methods have shown significant progress in data clustering tasks.
Deep clustering methods (including distance-based methods and subspace-based …

Deep clustering aims to promote clustering tasks by combining deep learning and clustering
together to learn the clustering-oriented representation, and many approaches have shown …

Federated learning has a significant advantage in protecting information privacy. Many
scholars proposed various secure learning methods within the framework of federated …

Subspace clustering (SC) aims to cluster data lying in a union of low-dimensional
subspaces. Usually, SC learns an affinity matrix and then performs spectral clustering. Both …

Deep learning-based clustering methods, especially those incorporating deep generative
models, have recently shown noticeable improvement on many multimedia benchmark …

Deep dictionary learning (DDL) aims to learn dictionaries at different levels and the deepest
level representations. However, existing DDL algorithms impose a-norm constraint on the …

Recently, deep dictionary learning (DDL) has aroused attention due to its abilities of
learning multiple different dictionaries and extracting multi-level abstract feature …

Recently, the tensor nuclear norm (TNN)-based tensor robust principle component analysis
(TRPCA) has achieved impressive performance in multidimensional data processing. The …

Radio map construction requires a large amount of radio measurement data with location
labels, which imposes a high deployment cost. This paper develops a region-based radio …

Recently, the transform-based tensor nuclear norm (TNN) methods have shown promising
performance and drawn increasing attention in tensor completion (TC) problems. The main …