Learning with Hilbert–Schmidt independence criterion: A review and new perspectives
T Wang, X Dai, Y Liu - Knowledge-based systems, 2021 - Elsevier
Abstract The Hilbert–Schmidt independence criterion (HSIC) was originally designed to
measure the statistical dependence of the distribution-based Hilbert space embedding in …
measure the statistical dependence of the distribution-based Hilbert space embedding in …
Generalized nonconvex low-rank tensor approximation for multi-view subspace clustering
The low-rank tensor representation (LRTR) has become an emerging research direction to
boost the multi-view clustering performance. This is because LRTR utilizes not only the …
boost the multi-view clustering performance. This is because LRTR utilizes not only the …
Dual shared-specific multiview subspace clustering
Multiview subspace clustering has received significant attention as the availability of diverse
of multidomain and multiview real-world data has rapidly increased in the recent years …
of multidomain and multiview real-world data has rapidly increased in the recent years …
Deep spectral representation learning from multi-view data
Multi-view representation learning (MvRL) aims to learn a consensus representation from
diverse sources or domains to facilitate downstream tasks such as clustering, retrieval, and …
diverse sources or domains to facilitate downstream tasks such as clustering, retrieval, and …
Jointly learning kernel representation tensor and affinity matrix for multi-view clustering
Multi-view clustering refers to the task of partitioning numerous unlabeled multimedia data
into several distinct clusters using multiple features. In this paper, we propose a novel …
into several distinct clusters using multiple features. In this paper, we propose a novel …
Semi-supervised multi-view deep discriminant representation learning
Learning an expressive representation from multi-view data is a key step in various real-
world applications. In this paper, we propose a semi-supervised multi-view deep …
world applications. In this paper, we propose a semi-supervised multi-view deep …
Feature concatenation multi-view subspace clustering
Multi-view clustering is a learning paradigm based on multi-view data. Since statistic
properties of different views are diverse, even incompatible, few approaches implement …
properties of different views are diverse, even incompatible, few approaches implement …
Multiview pca: A methodology of feature extraction and dimension reduction for high-order data
Z Xia, Y Chen, C Xu - IEEE Transactions on Cybernetics, 2021 - ieeexplore.ieee.org
Facing with rapidly increasing demands for analyzing high-order data or multiway data,
feature-extracting methods become imperative for analysis and processing. The traditional …
feature-extracting methods become imperative for analysis and processing. The traditional …
Multiview unsupervised shapelet learning for multivariate time series clustering
N Zhang, S Sun - IEEE Transactions on Pattern Analysis and …, 2022 - ieeexplore.ieee.org
Multivariate time series clustering has become an important research topic in the time series
learning task, which aims to discover the correlation among multiple sequences and …
learning task, which aims to discover the correlation among multiple sequences and …
Cross-Domain Few-Shot classification via class-shared and class-specific dictionaries
Abstract In Cross-Domain Few-Shot Classification, researchers mainly utilize models which
trained with source domain tasks to adapt to the target domain with very few samples, thus …
trained with source domain tasks to adapt to the target domain with very few samples, thus …