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

Ghanem, Sally; Panahi, Ashkan; Krim, Hamid; Kerekes, Ryan A.

doi:10.1109/jsen.2020.2999461

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

Journal Article · Thu Oct 15 00:00:00 EDT 2020 · IEEE Sensors Journal

DOI:https://doi.org/10.1109/jsen.2020.2999461· OSTI ID:1666012

^[1]; Panahi, Ashkan ^[2];

^[1];

^[3]

North Carolina State Univ., Raleigh, NC (United States)
North Carolina State Univ., Raleigh, NC (United States); Chalmers Univ. of Technology, Gothenburg (Sweden)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC05-00OR22725; NA0002576

OSTI ID:: 1666012

Journal Information:: IEEE Sensors Journal, Vol. 20, Issue 20; ISSN 1530-437X

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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