Results 71 to 80 of about 914,477 (196)
IEEE Access, 2018 Thousands of long non-coding RNAs (lncRNAs) are encoded by mammalian genomes and play important roles in various biological processes, including the regulation of gene transcription.
Chen Peng, Liang Zou, De-Shuang Huang
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, 2018 The tensor-tensor product (t-product) [M. E. Kilmer and C. D. Martin, 2011] is a natural generalization of matrix multiplication. Based on t-product, many operations on matrix can be extended to tensor cases, including tensor SVD, tensor spectral norm, tensor nuclear norm [C. Lu, et al., 2018] and many others.
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Tensor Complementarity Problem and Semi-positive Tensors
, 2015 The tensor complementarity problem $(\q, \mathcal{A})$ is to $$\mbox{ find } \x \in \mathbb{R}^n\mbox{ such that }\x \geq \0, \q + \mathcal{A}\x^{m-1} \geq \0, \mbox{ and }\x^\top (\q + \mathcal{A}\x^{m-1}) = 0.$$ We prove that a real tensor $\mathcal ...
Qi, Liqun, Song, Yisheng
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Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
Future Internet, 2019 Radio-frequency (RF) tomographic imaging is a promising technique for inferring multi-dimensional physical space by processing RF signals traversed across a region of interest. Tensor-based approaches for tomographic imaging are superior at detecting the
Da Xu, Tao Zhang
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Hankel Tensors: Associated Hankel Matrices and Vandermonde Decomposition
, 2014 Hankel tensors arise from applications such as signal processing. In this paper, we make an initial study on Hankel tensors. For each Hankel tensor, we associate it with a Hankel matrix and a higher order two-dimensional symmetric tensor, which we call ...
Qi, Liqun
core
Remote Sensing, 2019 Dimensionality reduction is of great importance which aims at reducing the spectral dimensionality while keeping the desirable intrinsic structure information of hyperspectral images. Tensor analysis which can retain both spatial and spectral information
Jinliang An +4 more
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Journal of machine learning research : JMLR, 2019 Statistical methods relating tensor predictors to scalar outcomes in a regression model generally vectorize the tensor predictor and estimate the coefficients of its entries employing some form of regularization, use summaries of the tensor covariate, or use a low dimensional approximation of the coefficient tensor.
Papadogeorgou, Georgia +2 more
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Unique characterization of the Bel-Robinson tensor
, 2003 We prove that a completely symmetric and trace-free rank-4 tensor is, up to sign, a Bel-Robinson type tensor, i.e., the superenergy tensor of a tensor with the same algebraic symmetries as the Weyl tensor, if and only if it satisfies a certain quadratic ...
Bergqvist G +8 more
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Brain-Apparatus CommunicationBackground Back-projection has been used to correct the variance and polarity indeterminacies for the independent component analysis. The variance and polarity of the components are essential features of neuroscience studies.Objective This work extends ...
Yuxing Hao +4 more
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Multi-View Tensor Sparse Representation Model for SAR Target Recognition
IEEE Access, 2019 The local structure feature of the target in synthetic aperture radar (SAR) image and the inner correlation among multiple SAR images of the same target can effectively improve the recognition performance.
Zhiqiang He +2 more
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