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Found 16 papers, 1 papers with code
Noisy Tensor Completion via Low-rank Tensor Ring
Experimental results on both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed model in recovering noisy incomplete tensor data compared with state-of-the-art tensor completion models.
Multi-view Data Classification with a Label-driven Auto-weighted Strategy
However, existing strategies cannot take advantage of semi-supervised information, only distinguishing the importance of views from a data feature perspective, which is often influenced by low-quality views then leading to poor performance.
FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
In PVEC, different PLOs recruit PVs as edge computing nodes for offloading services through an incentive mechanism, which is designed according to the computation demand and parking capacity constraints derived from FedParking.
Robust Output Regulation and Reinforcement Learning-based Output Tracking Design for Unknown Linear Discrete-Time Systems
To remove such a requirement, an off-policy reinforcement learning algorithm is proposed using only the measured output data along the trajectories of the system and the reference output.
Dynamical Systems
Graph Regularized Nonnegative Tensor Ring Decomposition for Multiway Representation Learning
Tensor ring (TR) decomposition is a powerful tool for exploiting the low-rank nature of multiway data and has demonstrated great potential in a variety of important applications.
An Efficient Tensor Completion Method via New Latent Nuclear Norm
In tensor completion, the latent nuclear norm is commonly used to induce low-rank structure, while substantially failing to capture the global information due to the utilization of unbalanced unfolding scheme.
Domain Confusion with Self Ensembling for Unsupervised Adaptation
Data collection and annotation are time-consuming in machine learning, expecially for large scale problem.
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition under Reshuffling
Exact recovery of tensor decomposition (TD) methods is a desirable property in both unsupervised learning and scientific data analysis.
Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization
Nonsmooth Nonnegative Matrix Factorization (nsNMF) is capable of producing more localized, less overlapped feature representations than other variants of NMF while keeping satisfactory fit to data.
Low-rank Multi-view Clustering in Third-Order Tensor Space
Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace.
Tensor Ring Decomposition
In this paper, we introduce a fundamental tensor decomposition model to represent a large dimensional tensor by a circular multilinear products over a sequence of low dimensional cores, which can be graphically interpreted as a cyclic interconnection of 3rd-order tensors, and thus termed as tensor ring (TR) decomposition.
Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices
Due to its promising classification performance, sparse representation based classification(SRC) algorithm has attracted great attention in the past few years.
Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds
Sparse subspace clustering (SSC), as one of the most successful subspace clustering methods, has achieved notable clustering accuracy in computer vision tasks.
Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data
With the increasing availability of various sensor technologies, we now have access to large amounts of multi-block (also called multi-set, multi-relational, or multi-view) data that need to be jointly analyzed to explore their latent connections.
Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness
Nonnegative Tucker decomposition (NTD) is a powerful tool for the extraction of nonnegative parts-based and physically meaningful latent components from high-dimensional tensor data while preserving the natural multilinear structure of data.
Accelerated Canonical Polyadic Decomposition by Using Mode Reduction
Canonical Polyadic (or CANDECOMP/PARAFAC, CP) decompositions (CPD) are widely applied to analyze high order tensors.
