@article{ghanem_panahi_krim_kerekes_2020, title={Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion}, volume={20}, ISSN={["1558-1748"]}, DOI={10.1109/JSEN.2020.2999461}, 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. 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.}, number={20}, journal={IEEE SENSORS JOURNAL}, author={Ghanem, Sally and Panahi, Ashkan and Krim, Hamid and Kerekes, Ryan A.}, year={2020}, pages={12307–12316} }
 @article{ghanem_krim_clouse_sakla_2018, title={Metric Driven Classification: A Non-Parametric Approach Based on the Henze-Penrose Test Statistic}, volume={27}, ISSN={["1941-0042"]}, DOI={10.1109/TIP.2018.2862352}, abstractNote={Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the usage of a non-parametric distribution-free metric, known as the Henze–Penrose test statistic to obtain bounds for the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbors (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-NN) classification accuracy. Simulation results demonstrate the effectiveness and the reliability of this metric in estimating the inter-class separability. In addition, the proposed bounds on the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-NN classification are exploited for evaluating the efficacy of different pre-processing techniques as well as selecting the least number of features that would achieve the desired classification performance.}, number={12}, journal={IEEE TRANSACTIONS ON IMAGE PROCESSING}, author={Ghanem, Sally and Krim, Hamid and Clouse, Hamilton Scott and Sakla, Wesam}, year={2018}, month={Dec}, pages={5947–5956} }