Browsing by Subject "data fusion"
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Item Heterogeneous Federated LearningYu, Fuxun; Zhang, Weishan; Qin, Zhuwei; Xu, Zirui; Wang, Di; Liu, Chenchen; Tian, Zhi; Chen, XiangFederated learning learns from scattered data by fusing collaborative models from local nodes. However, due to chaotic information distribution, the model fusion may suffer from structural misalignment with regard to unmatched parameters. In this work, we propose a novel federated learning framework to resolve this issue by establishing a firm structure-information alignment across collaborative models. Specifically, we design a feature-oriented regulation method ({Ψ-Net}) to ensure explicit feature information allocation in different neural network structures. Applying this regulating method to collaborative models, matchable structures with similar feature information can be initialized at the very early training stage. During the federated learning process under either IID or non-IID scenarios, dedicated collaboration schemes further guarantee ordered information distribution with definite structure matching, so as the comprehensive model alignment. Eventually, this framework effectively enhances the federated learning applicability to extensive heterogeneous settings, while providing excellent convergence speed, accuracy, and computation/communication efficiency.Item Multi-modal data fusion using source separation: Application to medical imaging(IEEE, 2015-08-17) Adali, Tulay; Levin-Schwartz, Yuri; Calhoun, Vince D.The Joint ICA (jICA) and the Transposed IVA (tIVA) models are two effective solutions based on blind source separation that enable fusion of data from multiple modalities in a symmetric and fully multivariate manner. In [1], their properties and the major issues in their implementation are discussed in detail. In this accompanying paper, we consider the application of these two models to fusion of multi-modal medical imaging data—functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) data collected from a group of healthy controls and patients with schizophrenia performing an auditory oddball task. We show how both models can be used to identify a set of components that report on differences between the two groups, jointly, for all the modalities used in the study. We discuss the importance of algorithm and order selection as well as trade-offs involved in the selection of one model over another. We note that for the selected dataset, especially given the limited number of subjects available for the study, jICA provides a more desirable solution, however the use of an ICA algorithm that uses flexible density matching provides advantages over the most widely used algorithm, Infomax, for the problem.Item Multimodal Data Fusion Using Source Separation: Two Effective Models Based on ICA and IVA and Their Properties(IEEE, 2015-09) Adali, Tulay; Levin-Schwartz, Yuri; Calhoun, Vince D.Fusion of information from multiple sets of data in order to extract a set of features that are most useful and relevant for the given task is inherent to many problems we deal with today. Since, usually, very little is known about the actual interaction among the data sets, it is highly desirable to minimize the underlying assumptions. This has been the main reason for the growing importance of data-driven methods, and in particular of independent component analysis (ICA) as it provides useful decompositions with a simple generative model and using only the assumption of statistical independence. A recent extension of ICA, independent vector analysis (IVA), generalizes ICA to multiple data sets by exploiting the statistical dependence across the data sets, and hence, as we discuss in this paper, provides an attractive solution to fusion of data from multiple data sets along with ICA. In this paper, we focus on two multivariate solutions for multimodal data fusion that let multiple modalities fully interact for the estimation of underlying features that jointly report on all modalities. One solution is the joint ICA model that has found wide application in medical imaging, and the second one is the transposed IVA model introduced here as a generalization of an approachItem Semantic Analytics on Social Networks: Experiences in Addressing the Problem of Conflict of Interest Detection(ACM, 2006-05-23) Aleman-Meza, Boanerges; Nagarajan, Meenakshi; Ramakrishnan, Cartic; Ding, Li; Kolari, Pranam; Sheth, Amit; Arpinar, Budak; Joshi, Anupam; Finin, TimIn this paper, we describe a Semantic Web application that detects Conflict of Interest relationships among potential reviewers and authors of scientific papers. This application discovers various "semantic associations" between the reviewers and authors in a populated ontology to determine a degree of Conflict of Interest. This ontology is built by integrating entities and relationships from two social networks, namely 'knows' from a FOAF (Friendof- a-Friend) social network, and 'co-author' from the underlying co-authorship network of the DBLP bibliography. We describe our experiences on development of this application in the context of a class of Semantic Web applications which have important research and engineering challenges in common. In addition, we present an evaluation of our approach for real-life COI detection.