About: Tendi: Tensor Disaggregation from Multiple Coarse Views

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Tendi: Tensor Disaggregation from Multiple Coarse Views
Creator	Almutairi, Faisal Kanatsoulis, Charilaos Sidiropoulos, Nicholas
Source	PMC
abstract	Multidimensional data appear in various interesting applications, e.g., sales data indexed by stores, items, and time. Oftentimes, data are observed aggregated over multiple data atoms, thus exhibit low resolution. Temporal aggregation is most common, but many datasets are also aggregated over other attributes. Multidimensional data, in particular, are sometimes available in multiple coarse views, aggregated across different dimensions – especially when sourced by different agencies. For instance, item sales can be aggregated temporally, and over groups of stores based on their location or affiliation. However, data in finer granularity significantly benefit forecasting and data analytics, prompting increasing interest in data disaggregation methods. In this paper, we propose Tendi, a principled model that efficiently disaggregates multidimensional (tensor) data from multiple views, aggregated over different dimensions. Tendi employs coupled tensor factorization to fuse the multiple views and provide recovery guarantees under realistic conditions. We also propose a variant of Tendi, called TendiB, which performs the disaggregation task without any knowledge of the aggregation mechanism. Experiments on real data from different domains demonstrate the high effectiveness of the proposed methods.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47436-2_65
has license	no-cc
sha1sum (hex)	a8b3d74cc9815d172cd5cf549aa3098a75a2ab4d
schema:url	https://doi.org/10.1007/978-3-030-47436-2_65
resource representing a document's title	Tendi: Tensor Disaggregation from Multiple Coarse Views
has PubMed Central identifier	PMC7206359
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:a8b3d74cc9815d172cd5cf549aa3098a75a2ab4d#body_text
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