GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries

By U. Kang, Evangelos E. Papalexakis, Abhay Harpale, Christos Faloutsos. 2012

304 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mathematics, Hardware and Architecture

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Citations in the corpus

Federated Tensor Factorization for Computational Phenotyping (2017, background): “is is dierent from distributed tensor factorization models [7, 16] and grid tensor factorization models [9].”
Distributed large-scale tensor decomposition (2014, methodology): “In [10] a smart ordering of computations is proposed and explicit computation of Khatri-Rao matrix product is avoided.”
Tensor Decomposition for Signal Processing and Machine Learning (2016, methodology): “Furthermore, the computation can be parallelized in several ways – see [77], [80]–[83] for various resource-efficient algorithms for matricized tensor times Khatri–Rao product (MTTKRP) computations.”
L G ] 1 1 A pr 2 01 7 Federated Tensor Factorization for Computational Phenotyping (2018, unknown): “is is different from distributed tensor factorization models [7, 16] and grid tensor factorization models [9].”
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions (2016, unknown): “Alternative algorithms for the CP decomposition of huge-scale tensors, include tensor sketching – a random mapping technique, which exploits kernel methods and regression [278], and the class of distributed algorithms such as the CPOPT [4], the DFacTo [45] and the GigaTensor which is based on Hadoop”
Typed Tensor Decomposition of Knowledge Bases for Relation Extraction (2014, background): “2009; Riedel et al., 2010; Ritter et al., 2013), an existing knowledge base is typically not involved in the process of relation extraction.”
Tensors for Data Mining and Data Fusion (2016, methodology): “Kang et al. [2012] and Papalexakis et al. [2012] apply the CP decomposition to data coming from the Read the Web [RTW 2016] project.”
DFacTo: Distributed Factorization of Tensors (2014, methodology): “Two papers that are most closely related to our work are the GigaTensor algorithm proposed by [8] and the Sparse Tensor Toolbox of [7].”
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives (2017, methodology): “…and Khoromskij, 2013, Khoromskij, 2011b, Khoromskij and Miao, 2014], • CP decomposition and its applications [Choi and Vishwanathan, 2014, Kamal et al., 2016, Kang et al., 2012, Shin et al., 2017, Vervliet and Lathauwer, 2016, Wang et al., 2015, Wetzstein et al., 2012], • Tucker decompositions…”
HiCOO: Hierarchical Storage of Sparse Tensors (2018, methodology): “GigaTensor [31] reformulated MTTKRP as a series of Hadamard products to utilize the massive parallelism of MapReduce.”
Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization (2014, methodology): “The CP tensor decomposition has been used for concept discovery [16], network analysis of fMRI data [9], and community discovery [20].”