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Title: Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

Abstract

Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of identifying more complex trends in data sets than simple linear models. In this work, we build on and extend RoSuRe to prospect the structure of different data modalities individually. We propose a novel multi-modal data fusion approach based on group sparsity which we refer to as Robust Group Subspace Recovery (RoGSuRe). Relying on a bi-sparsity pursuit paradigm and non-smooth optimization techniques, the introduced framework learns a new joint representation of the time series from different data modalities, respecting an underlying UoS model. We subsequently integrate the obtained structures to form a unified subspace structure. The proposed approach exploits the structural dependencies between the different modalities data to cluster the associated target objects. The resulting fusion of the unlabeled sensors’ data from experiments on audio and magnetic data has shown that our method is competitive with other state of the art subspace clustering methods. The resulting UoS structure is employed to classify newly observed data points, highlighting the abstraction capacity of the proposed method.

Authors:
ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [3]
  1. North Carolina State Univ., Raleigh, NC (United States)
  2. North Carolina State Univ., Raleigh, NC (United States); Chalmers Univ. of Technology, Gothenburg (Sweden)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1666012
Grant/Contract Number:  
AC05-00OR22725; NA0002576
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Sensors Journal
Additional Journal Information:
Journal Volume: 20; Journal Issue: 20; Journal ID: ISSN 1530-437X
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
Sensor fusion; data integration; data models; sparse matrices; magnetic sensors; sensor phenomena and characterization; sparse learning; unsupervised classification; data fusion; multimodal data

Citation Formats

Ghanem, Sally, Panahi, Ashkan, Krim, Hamid, and Kerekes, Ryan A. Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion. United States: N. p., 2020. Web. doi:10.1109/jsen.2020.2999461.
Ghanem, Sally, Panahi, Ashkan, Krim, Hamid, & Kerekes, Ryan A. Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion. United States. doi:10.1109/jsen.2020.2999461.
Ghanem, Sally, Panahi, Ashkan, Krim, Hamid, and Kerekes, Ryan A. Thu . "Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion". United States. doi:10.1109/jsen.2020.2999461.
@article{osti_1666012,
title = {Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion},
author = {Ghanem, Sally and Panahi, Ashkan and Krim, Hamid and Kerekes, Ryan A.},
abstractNote = {Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of identifying more complex trends in data sets than simple linear models. In this work, we build on and extend RoSuRe to prospect the structure of different data modalities individually. We propose a novel multi-modal data fusion approach based on group sparsity which we refer to as Robust Group Subspace Recovery (RoGSuRe). Relying on a bi-sparsity pursuit paradigm and non-smooth optimization techniques, the introduced framework learns a new joint representation of the time series from different data modalities, respecting an underlying UoS model. We subsequently integrate the obtained structures to form a unified subspace structure. The proposed approach exploits the structural dependencies between the different modalities data to cluster the associated target objects. The resulting fusion of the unlabeled sensors’ data from experiments on audio and magnetic data has shown that our method is competitive with other state of the art subspace clustering methods. The resulting UoS structure is employed to classify newly observed data points, highlighting the abstraction capacity of the proposed method.},
doi = {10.1109/jsen.2020.2999461},
journal = {IEEE Sensors Journal},
number = 20,
volume = 20,
place = {United States},
year = {2020},
month = {10}
}

Journal Article:
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This content will become publicly available on October 15, 2021
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