2018
DOI: 10.1371/journal.pone.0200579
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CTD: Fast, accurate, and interpretable method for static and dynamic tensor decompositions

Abstract: How can we find patterns and anomalies in a tensor, i.e., multi-dimensional array, in an efficient and directly interpretable way? How can we do this in an online environment, where a new tensor arrives at each time step? Finding patterns and anomalies in multi-dimensional data have many important applications, including building safety monitoring, health monitoring, cyber security, terrorist detection, and fake user detection in social networks. Standard tensor decomposition results are not directly interpret… Show more

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Cited by 8 publications
(7 citation statements)
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“…powerful tool to analyze compound-gene, compound-protein, compound-disease, and gene-disease relationships. ese data can be combined with gene function and signaling pathways to predict the mechanism of action of the drugs in various diseases [33]. e abovementioned interactions for curcumin were searched in the CTD, which returned 889 interacting genes.…”
Section: Target Genes Prediction Of Curcumin On Ec Ctd Is Amentioning
confidence: 99%
“…powerful tool to analyze compound-gene, compound-protein, compound-disease, and gene-disease relationships. ese data can be combined with gene function and signaling pathways to predict the mechanism of action of the drugs in various diseases [33]. e abovementioned interactions for curcumin were searched in the CTD, which returned 889 interacting genes.…”
Section: Target Genes Prediction Of Curcumin On Ec Ctd Is Amentioning
confidence: 99%
“…In Fig. 10, we can see the plot of the matrix C after ll1 function in cpd mode was applied, where L = [9,5,3,6] is the vector which contains the number of elements that each cluster has. In Table III we have the output of the ll1 function.…”
Section: Resultsmentioning
confidence: 99%
“…In Fig. 11, we can see the plot of the matrix C after ll1 function in cpd mode which was applied using 6 clusters and L = [4,4,1,5,3,6].…”
Section: Resultsmentioning
confidence: 99%
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“…We use image inpainting and link prediction to perform the experiments for performance comparison. Our competitors include (i) SPC [33], which applies the total variation constraint to the low-rank model for tensor completion; (ii) TR-LS [16], which adopts a dual framework to solve the low-rank tensor completion; (iii) Rprecon [17], which is a Riemannian manifold preconditioning approach for tensor completion; (iv) GeomCG [18], which applies Riemannian optimization to the completion of fixed-rank tensor; (v) FFW [28], which uses the Frank-Wolfe algorithm and scaled latent nuclear norm for tensor completion; and (vi) CTD-S [34], which accelerates tensor decomposition by removing redundancy. They all need to tune several parameters.…”
Section: Performance Comparisonmentioning
confidence: 99%