Field-of-Experts Filters Guided Tensor Completion

Xiong, Boli; Liu, Qiegen; Xiong, Jie; Li, Sanqian; Wang, Rongpin; Liang, Dong

doi:10.1109/tmm.2018.2806225

Cited by 19 publications

(3 citation statements)

References 45 publications

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“…ough the techniques varied, both experimental results and theoretical analysis re ect a natural and intuitive phenomenon that: with the increasing ratio of missing entries, the prediction accuracy tends to be signi cantly decreased. In real-world data-driven applications, besides the target tensor object, additional side information such as spatial and temporal similarities among objects or auxiliary coupled matrices/tensors may also exist [58,59,139,200,206]. ese heterogeneous data sources are usually bonded and compensate with each other and could serve as potential supplements to improve the completion quality especially when the missing ratio of the target tensor is high.…”

Section: Tensor Completion With Auxiliary Informationmentioning

confidence: 99%

Tensor Completion Algorithms in Big Data Analytics

Song

Caverlee

et al. 2019

ACM Trans. Knowl. Discov. Data

166

118

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Tensor completion is a problem of lling the missing or unobserved entries of partially observed tensors. Due to the multidimensional character of tensors in describing complex datasets, tensor completion algorithms and their applications have received wide a ention and achievement in areas like data mining, computer vision, signal processing, and neuroscience. In this survey, we provide a modern overview of recent advances in tensor completion algorithms from the perspective of big data analytics characterized by diverse variety, large volume, and high velocity. We characterize these advances from four perspectives: general tensor completion algorithms, tensor completion with auxiliary information (variety), scalable tensor completion algorithms (volume), and dynamic tensor completion algorithms (velocity). Further, we identify several tensor completion applications on real-world data-driven problems and present some common experimental frameworks popularized in the literature. Our goal is to summarize these popular methods and introduce them to researchers and practitioners for promoting future research and applications. We conclude with a discussion of key challenges and promising research directions in this community for future exploration.

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Section: Tensor Completion With Auxiliary Informationmentioning

confidence: 99%

Tensor Completion Algorithms in Big Data Analytics

Song

Caverlee

et al. 2019

ACM Trans. Knowl. Discov. Data

166

118

View full text Add to dashboard Cite

show abstract

“…Visible light images are used to extract features and classify leaves [17]. Lidar is applied for surface trait extraction and the classification of 3D objects [18][19][20][21]. However, these techniques do not perform well on occluded or inner parts [18].…”

Section: Related Workmentioning

confidence: 99%

3D Morphological Processing for Wheat Spike Phenotypes Using Computed Tomography Images

et al. 2019

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Wheat is the main food crop today world-wide. In order to improve its yields, researchers are committed to understand the relationships between wheat genotypes and phenotypes. Compared to progressive technology of wheat gene section identification, wheat trait measurement is mostly done manually in a destructive, labor-intensive and time-consuming way. Therefore, this study will be greatly accelerated and promoted if we can automatically discover wheat phenotype in a nondestructive and fast manner. In this paper, we propose a novel pipeline based on 3D morphological processing to detect wheat spike grains and stem nodes from 3D X-ray micro computed tomography (CT) images. We also introduce a set of newly defined 3D phenotypes, including grain aspect ratio, porosity, Grain-to-Grain distance, and grain angle, which are very difficult to be manually measured. The analysis of the associations among these traits would be very helpful for wheat breeding. Experimental results show that our method is able to count grains more accurately than normal human performance. By analyzing the relationships between traits and environment conditions, we find that the Grain-to-Grain distance, aspect ratio and porosity are more likely affected by the genome than environment (only tested temperature and water conditions). We also find that close grains will inhibit grain volume growth and that the aspect ratio 3.5 may be the best for higher yield in wheat breeding.

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“…Introduction. Tensor completion aims at estimating missing entries or damaged parts in high-dimensional data and plays an important role in computer vision, e.g., color image inpainting [3,31,35,52,58], video inpainting [5,8,26,53,60], hyperspectral images recovery [27,34,50,51], higher-order web link analysis [28,36], multi-linear system [9], and seismic data reconstruction [16]. As a typical ill-posed inverse problem, stable tensor completion processes usually rely on prior knowledge of the underlying tensor.…”

mentioning

confidence: 99%