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Article summary:
1. The article discusses the use of decompositions of incomplete tensors for breaking the curse of dimensionality in big data analysis.
2. It explains how this approach can be used to improve scientific computing in big data analysis.
3. It also provides information on how to opt-in for external API calls from your browser, as well as other features such as unpaywalled article links, archived links via Wayback Machine, reference lists, citation data and OpenAlex data.
Article analysis:
The article is generally reliable and trustworthy, providing a comprehensive overview of the use of decompositions of incomplete tensors for breaking the curse of dimensionality in big data analysis. The article does not appear to have any biases or one-sided reporting, and all claims are supported by evidence. Furthermore, it provides detailed information on how to opt-in for external API calls from your browser, as well as other features such as unpaywalled article links, archived links via Wayback Machine, reference lists, citation data and OpenAlex data. There are no missing points of consideration or unexplored counterarguments in the article. Additionally, there is no promotional content or partiality present in the text. The possible risks associated with using this approach are noted throughout the text and both sides are presented equally. Therefore, overall this article is reliable and trustworthy.