research-article

Focusing Decomposition Accuracy by Personalizing Tensor Decomposition (PTD)

Authors:

Kasim Selçuk Candan,

Maria Luisa SapinoAuthors Info & Claims

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Pages 689 - 698

https://doi.org/10.1145/2661829.2662051

Published: 03 November 2014 Publication History

Abstract

Tensor decomposition operation is the basis for many data analysis tasks from clustering, trend detection, anomaly detection, to correlation analysis. One key problem with tensor decomposition, however, is its computational complexity -- especially for dense data sets, the decomposition process takes exponential time in the number of tensor modes; the process is relatively faster for sparse tensors, but decomposition is still a major bottleneck in many applications. While it is possible to reduce the decomposition time by trading performance with decomposition accuracy, a drop in accuracy may not always be acceptable. In this paper, we first recognize that in many applications, the user may have a focus of interest -- i.e., part of the data for which the user needs high accuracy -- and beyond this area focus, accuracy may not be as critical. Relying on this observation, we propose a novel Personalized Tensor Decomposition(PTD) mechanism for accounting for the user's focus: PTD takes as input one or more areas of focus and performs the decomposition in such a way that, when reconstructed, the accuracy of the tensor is boosted for these areas of focus. We discuss alternative ways PTD can be implemented. Experiments show that PTD helps boost accuracy at the foci of interest, while reducing the overall tensor decomposition time.

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https://dl.acm.org/doi/10.1145/3672556
Dey L(2024)Knowledge graph‐driven data processing for business intelligenceWIREs Data Mining and Knowledge Discovery10.1002/widm.152914:3Online publication date: 11-Feb-2024
https://doi.org/10.1002/widm.1529
Garg Y(2021)ReTriM: Reconstructive Triplet Loss for Learning Reduced Embeddings for Multi-Variate Time Series2021 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW53433.2021.00062(460-465)Online publication date: Dec-2021
https://doi.org/10.1109/ICDMW53433.2021.00062
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Focusing Decomposition Accuracy by Personalizing Tensor Decomposition (PTD)

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Published In

cover image ACM Conferences

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

November 2014

2152 pages

ISBN:9781450325981

DOI:10.1145/2661829

General Chairs:
Jianzhong Li
Harbin Inst. of Technology
,
X. Sean Wang
Fudan University
,
Program Chairs:
Minos Garofalakis
Technical University of Crete, Greece
,
Ian Soboroff
National Institute of Standards, USA
,
Torsten Suel
New York University, USA
,
Min Wang
Google Research, USA

Copyright © 2014 ACM.

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Published: 03 November 2014

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CIKM '14

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CIKM '14: 2014 ACM Conference on Information and Knowledge Management

November 3 - 7, 2014

Shanghai, China

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CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Azad FCandan KKapkiç ALi MLiu HMandal PSheth PArslan BChowell-Puente GSabo JMuenich RRedondo Anton JSapino M(2024)(Vision Paper) A Vision for Spatio-Causal Situation Awareness, Forecasting, and PlanningACM Transactions on Spatial Algorithms and Systems10.1145/367255610:2(1-42)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3672556
Dey L(2024)Knowledge graph‐driven data processing for business intelligenceWIREs Data Mining and Knowledge Discovery10.1002/widm.152914:3Online publication date: 11-Feb-2024
https://doi.org/10.1002/widm.1529
Garg Y(2021)ReTriM: Reconstructive Triplet Loss for Learning Reduced Embeddings for Multi-Variate Time Series2021 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW53433.2021.00062(460-465)Online publication date: Dec-2021
https://doi.org/10.1109/ICDMW53433.2021.00062
Li XCandan KSapino M(2020)Noise Adaptive Tensor Train Decomposition for Low-Rank Embedding of Noisy DataSimilarity Search and Applications10.1007/978-3-030-60936-8_16(203-217)Online publication date: 14-Oct-2020
https://doi.org/10.1007/978-3-030-60936-8_16
Wang YWang SQi GTang JLi B(2018)Weakly Supervised Facial Attribute Manipulation via Deep Adversarial Network2018 IEEE Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV.2018.00019(112-121)Online publication date: Mar-2018
https://doi.org/10.1109/WACV.2018.00019
Li XCandan KSapino M(2018)M2TD: Multi-Task Tensor Decomposition for Sparse Ensemble Simulations2018 IEEE 34th International Conference on Data Engineering (ICDE)10.1109/ICDE.2018.00106(1144-1155)Online publication date: Apr-2018
https://doi.org/10.1109/ICDE.2018.00106
Candan KHuang SLi XSapino M(2018)Effective Tensor-Based Data Clustering Through Sub-Tensor Impact GraphsClustering Methods for Big Data Analytics10.1007/978-3-319-97864-2_7(145-179)Online publication date: 28-Oct-2018
https://doi.org/10.1007/978-3-319-97864-2_7
Li XCandan KSapino MBarrett RCummings RAgichtein EGabrilovich E(2017)nTDProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052641(243-252)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3038912.3052641
Wang YTang JLi JLi BWan YMellina CO'Hare NChang YBarrett RCummings RAgichtein EGabrilovich E(2017)Understanding and Discovering Deliberate Self-harm Content in Social MediaProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052555(93-102)Online publication date: 3-Apr-2017
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Tay YLuu AHui SBrauer Fde Rijke MShokouhi MTomkins AZhang M(2017)Random Semantic Tensor Ensemble for Scalable Knowledge Graph Link PredictionProceedings of the Tenth ACM International Conference on Web Search and Data Mining10.1145/3018661.3018695(751-760)Online publication date: 2-Feb-2017
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