Tensor Factorization for Low-Rank Tensor Completion

By Pan Zhou, Canyi Lu, Zhouchen Lin, Chao Zhang. IEEE Transactions on Image Processing, 2018

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T-Jordan Canonical Form and T-Drazin Inverse Based on the T-Product (2019, background): “he outer inverse by Einstein product is developed by Stanimirovic et al. [56] The tensor T-product introduced by Kilmer [34] has been proved to be of great use in many areas, such as image processing [33, 34, 49, 55, 59, 70], computer vision [20, 64], signal processing [12, 41, 44, 58], low rank ten”
Essential Tensor Learning for Multi-View Spectral Clustering (2018, background): “While multi-view representations also contain view-speciﬁc information, we hope to explore the high order correlation and ﬁnd the principle components [8]–[11] of multi-view representations from the tensor aspect based on the Markov chain clustering.”
Generalized tensor function via the tensor singular value decomposition based on the T-product (2019, methodology): “Recently, the tensor T-product has been established and proved to be a useful tool in many areas, such as image processing [27, 28, 40, 44, 46, 56], computer vision [4, 16, 49, 51], signal processing [9, 33, 36, 45], low rank tensor recovery and robust tensor PCA [31, 33], data completion and…”