Tensors for Data Mining and Data Fusion
By Evangelos E. Papalexakis, Christos Faloutsos, N. Sidiropoulos. 2016
405 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Mathematics, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Marinka Zitnik
- Jure Leskovec
- Zachary Chase Lipton
- Anima Anandkumar
- Monica Agrawal
- William L. Hamilton
- Jean Kossaifi
- Saba A. Al-Sayouri
In Computational Mathematics
- Jure Leskovec
- Monica Agrawal
- Marinka Zitnik
- Mengchu Zhou
- Aran Khanna
- Xin Luo
- Anima Anandkumar
- Tommaso Furlanello
In Radiology, Nuclear Medicine and Imaging
- Marinka Zitnik
- Jure Leskovec
- Monica Agrawal
- Evangelos E. Papalexakis
- Jean Kossaifi
- Zachary Chase Lipton
Citations in the corpus
- Modeling polypharmacy side effects with graph convolutional networks (2018, methodology): “The final network after linking entity vocabularies used by different databases has 645 drug and 19,085 protein nodes connected by 715,612 protein-protein, 4,651,131 drug-drug, and 18,596 drug-protein edges.”
- Smart Infrastructure Maintenance Using Incremental Tensor Analysis: Extended Abstract (2017, background): “Tensor analysis has produced high-quality results at state-of-the-art performances in many applications such as social network and brain data analysis, web mining and information retrieval, and health care analytics [20].”
- All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs (2020, background): “For more details about tensors and tensor decomposition, we refer the interested reader to [20, 28].”
- Tensor Regression Networks (2017, background): “, 2015), data mining and fusion (Papalexakis et al., 2016), blind source separation (Cichocki et al.”
- Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions (2016, neutral): “Review and tutorial papers (Kolda and Bader, 2009; Lu et al., 2011; Grasedyck et al., 2013; Cichocki et al., 2015b; de Almeida et al., 2015; Sidiropoulos et al., 2016; Papalexakis et al., 2016; Bachmayr et al., 2016) and books (Smilde et al.”
- Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors (2020, background): “Meanwhile, the mathematical form of CPF is much more concise than that of TF [27], [28].”