ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2018
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records.Proceedings of the ACM Conference on Health, Inference, and Learning · 2020Article
- COPA: Constrained PARAFAC2 for Sparse & Large Datasets.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2018Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
This paper presents a new method, which we call SUSTain, that extends real-valued matrix and tensor factorizations to data where values are integers. Such data are common when the values correspond to event counts or ordinal measures. The conventional approach is to treat integer data as real, and then apply real-valued factorizations. However, doing so fails to preserve important characteristics of the original data, thereby making it hard to interpret the results. Instead, our approach extracts factor values from integer datasets as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.