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Found 9 papers, 3 papers with code
S^2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering
Specifically, we first construct the embedding feature tensor by stacking the embedding features of different views into a tensor and rotating it.
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction
In recent years, the fusion of high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI) has been recognized as an effective method for HSI super-resolution (HSI-SR).
Tensor Regression
Tensors, as high dimensional extensions of vectors, are considered as natural representations of high dimensional data.
Multi-view MERA Subspace Clustering
Benefiting from multiple interactions among orthogonal/semi-orthogonal (low-rank) factors, the low-rank MERA has a strong representation power to capture the complex inter/intra-view information in the self-representation tensor.
Adaptively Topological Tensor Network for Multi-view Subspace Clustering
Therefore, a pre-defined tensor decomposition may not fully exploit low rank information for a certain dataset, resulting in sub-optimal multi-view clustering performance.
Tucker-O-Minus Decomposition for Multi-view Tensor Subspace Clustering
To alleviate these problems, we propose a new tensor decomposition called Tucker-O-Minus Decomposition (TOMD) for multi-view clustering.
URIR: Recommendation algorithm of user RNN encoder and item encoder based on knowledge graph
This implies that URIR can effectively use knowledge graph to obtain better user codes and item codes, thereby obtaining better recommendation results.
Hyperspectral Image Denoising with Partially Orthogonal Matrix Vector Tensor Factorization
In this paper, we present a HSI restoration method named smooth and robust low rank tensor recovery.
Bayesian Low Rank Tensor Ring Model for Image Completion
Low rank tensor ring model is powerful for image completion which recovers missing entries in data acquisition and transformation.
