{"title":"面向大数据处理的快速张量分解","authors":"V. Nguyen, K. Abed-Meraim, N. Linh-Trung","doi":"10.1109/ATC.2016.7764776","DOIUrl":null,"url":null,"abstract":"Tensors, as a natural extension of matrices, and their decompositions provide important tools in many disciplines such as psychometrics, signal processing, data communication, computer vision, and machine learning. The main objective of this paper is to briefly review several recent state-of-the-art approaches for large-scale tensor data which is a crucial part of big data. Moreover, we also introduce our own contributions on this topic.","PeriodicalId":225413,"journal":{"name":"2016 International Conference on Advanced Technologies for Communications (ATC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Fast tensor decompositions for big data processing\",\"authors\":\"V. Nguyen, K. Abed-Meraim, N. Linh-Trung\",\"doi\":\"10.1109/ATC.2016.7764776\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Tensors, as a natural extension of matrices, and their decompositions provide important tools in many disciplines such as psychometrics, signal processing, data communication, computer vision, and machine learning. The main objective of this paper is to briefly review several recent state-of-the-art approaches for large-scale tensor data which is a crucial part of big data. Moreover, we also introduce our own contributions on this topic.\",\"PeriodicalId\":225413,\"journal\":{\"name\":\"2016 International Conference on Advanced Technologies for Communications (ATC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Advanced Technologies for Communications (ATC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ATC.2016.7764776\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Advanced Technologies for Communications (ATC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATC.2016.7764776","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast tensor decompositions for big data processing
Tensors, as a natural extension of matrices, and their decompositions provide important tools in many disciplines such as psychometrics, signal processing, data communication, computer vision, and machine learning. The main objective of this paper is to briefly review several recent state-of-the-art approaches for large-scale tensor data which is a crucial part of big data. Moreover, we also introduce our own contributions on this topic.