The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Yuanchung Sheu - One of the best experts on this subject based on the ideXlab platform.
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diffuse to fuse eeg spectra Intrinsic Geometry of sleep dynamics for classification
Biomedical Signal Processing and Control, 2020Co-Authors: Gi Ren Liu, John Malik, Yuanchung SheuAbstract:Abstract We propose a novel algorithm for sleep dynamics visualization and automatic annotation by applying diffusion Geometry based sensor fusion algorithm to fuse spectral information from two electroencephalograms (EEG). The diffusion Geometry approach helps organize the nonlinear dynamical structure hidden in the EEG signal. The visualization is achieved by the nonlinear dimension reduction capability of the chosen diffusion Geometry algorithms. For the automatic annotation purpose, the support vector machine is trained to predict the sleep stage. The prediction performance is validated on a publicly available benchmark database, Physionet Sleep-EDF [extended] SC* (SC = Sleep Cassette) and ST* (ST = Sleep Telemetry), with the leave-one-subject-out cross validation. When we have a single EEG channel (Fpz-Cz), the overall accuracy, macro F1 and Cohen's kappa achieve 82.72%, 75.91% and 76.1% respectively in Sleep-EDF SC* and 78.63%, 73.58% and 69.48% in Sleep-EDF ST*. This performance is compatible with the state-of-the-art results. When we have two EEG channels (Fpz-Cz and Pz-Oz), the overall accuracy, macro F1 and Cohen's kappa achieve 84.44%, 78.25% and 78.36% respectively in Sleep-EDF SC* and 79.05%, 74.73% and 70.31% in Sleep-EDF ST*. The results suggest the potential of the proposed algorithm in practical applications.
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explore Intrinsic Geometry of sleep dynamics and predict sleep stage by unsupervised learning techniques
arXiv: Signal Processing, 2019Co-Authors: Gi Ren Liu, Yuanchung SheuAbstract:We propose a novel unsupervised approach for sleep dynamics exploration and automatic annotation by combining modern harmonic analysis tools. Specifically, we apply diffusion-based algorithms, diffusion map (DM) and alternating diffusion (AD) algorithms, to reconstruct the Intrinsic Geometry of sleep dynamics by reorganizing the spectral information of an electroencephalogram (EEG) extracted from a nonlinear-type time frequency analysis tool, the synchrosqueezing transform (SST). The visualization is achieved by the nonlinear dimension reduction properties of DM and AD. Moreover, the reconstructed nonlinear geometric structure of the sleep dynamics allows us to achieve the automatic annotation purpose. The hidden Markov model is trained to predict the sleep stage. The prediction performance is validated on a publicly available benchmark database, Physionet Sleep-EDF [extended] SC* and ST*, with the leave-one-subject-out cross validation. The overall accuracy and macro F1 achieve 82:57% and 76% in Sleep-EDF SC* and 77.01% and 71:53% in Sleep-EDF ST*, which is compatible with the state-of-the-art results by supervised learning-based algorithms. The results suggest the potential of the proposed algorithm for clinical applications.
Junbin Gao - One of the best experts on this subject based on the ideXlab platform.
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kernel clustering on symmetric positive definite manifolds via double approximated low rank representation
International Conference on Multimedia and Expo, 2020Co-Authors: Xinglin Piao, Junbin Gao, Yanfeng Sun, Xin Yang, Baocai Yin, Wenwu ZhuAbstract:As an effective descriptor, Symmetric Positive Definite (SPD) matrix is widely used in several areas such as image clustering. Recently, researchers proposed some effective methods based on low rank theory for SPD data clustering with nonlinear metric. However, single nuclear norm is always adopted to formulate the low rank model in these methods, which would lead to suboptimal solution. In this paper, we proposed a novel double low rank representation method for SPD clustering problem, in which matrix factorization and nonconvex rank constraint are combined to reveal the Intrinsic property of the data instead of employing the nuclear norm. Meanwhile, kernel method and Log-Euclidean metric are combined to better explore the Intrinsic Geometry within SPD data. The proposed method has been evaluated on several public datasets and the experimental results demonstrate that the proposed method outperforms the state-of-the-art ones.
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low rank representation on riemannian manifold of symmetric positive definite matrices
SIAM International Conference on Data Mining, 2015Co-Authors: Junbin Gao, Xia Hong, David TienAbstract:Sparse coding aims to find a more compact representation based on a set of dictionary atoms. A well-known technique looking at 2D sparsity is the low rank representation (LRR). However, in many computer vision applications, data often originate from a manifold, which is equipped with some Riemannian Geometry. In this case, the existing LRR becomes inappropriate for modeling and incorporating the Intrinsic Geometry of the manifold that is potentially important and critical to applications. In this paper, we generalize the LRR over the Euclidean space to the LRR model over a specific Rimannian manifold—the manifold of symmetric positive matrices (SPD). Experiments on several computer vision datasets showcase its noise robustness and superior performance on classification and segmentation compared with state-of-the-art approaches.
Ronen Talmon - One of the best experts on this subject based on the ideXlab platform.
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parsimonious representation of nonlinear dynamical systems through manifold learning a chemotaxis case study
Applied and Computational Harmonic Analysis, 2018Co-Authors: Carmeline J Dsilva, Ronen Talmon, Ronald R Coifman, Ioannis G KevrekidisAbstract:Nonlinear manifold learning algorithms, such as diffusion maps, have been fruitfully applied in recent years to the analysis of large and complex data sets. However, such algorithms still encounter challenges when faced with real data. One such challenge is the existence of “repeated eigendirections,” which obscures the detection of the true dimensionality of the underlying manifold and arises when several embedding coordinates parametrize the same direction in the Intrinsic Geometry of the data set. We propose an algorithm, based on local linear regression, to automatically detect coordinates corresponding to repeated eigendirections. We construct a more parsimonious embedding using only the eigenvectors corresponding to unique eigendirections, and we show that this reduced diffusion maps embedding induces a metric which is equivalent to the standard diffusion distance. We first demonstrate the utility and flexibility of our approach on synthetic data sets. We then apply our algorithm to data collected from a stochastic model of cellular chemotaxis, where our approach for factoring out repeated eigendirections allows us to detect changes in dynamical behavior and the underlying Intrinsic system dimensionality directly from data.
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assess sleep stage by modern signal processing techniques
arXiv: Medical Physics, 2014Co-Authors: Ronen TalmonAbstract:In this paper, two modern adaptive signal processing techniques, Empirical Intrinsic Geometry and Synchrosqueezing transform, are applied to quantify different dynamical features of the respiratory and electroencephalographic signals. We show that the proposed features are theoretically rigorously supported, as well as capture the sleep information hidden inside the signals. The features are used as input to multiclass support vector machines with the radial basis function to automatically classify sleep stages. The effectiveness of the classification based on the proposed features is shown to be comparable to human expert classification -- the proposed classification of awake, REM, N1, N2 and N3 sleeping stages based on the respiratory signal (resp. respiratory and EEG signals) has the overall accuracy $81.7\%$ (resp. $89.3\%$) in the relatively normal subject group. In addition, by examining the combination of the respiratory signal with the electroencephalographic signal, we conclude that the respiratory signal consists of ample sleep information, which supplements to the information stored in the electroencephalographic signal.
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nonlinear modeling and processing using empirical Intrinsic Geometry with application to biomedical imaging
International Conference on Geometric Science of Information, 2013Co-Authors: Ronen Talmon, Yoel Shkolnisky, Ronald R CoifmanAbstract:In this paper we present a method for Intrinsic modeling of nonlinear filtering problems without a-priori knowledge using empirical information Geometry and empirical differential Geometry. We show that the inferred model is noise resilient and invariant under different random observations and instrumental modalities. In addition, we show that it can be extended efficiently to newly acquired measurements. Based on this model, we present a Bayesian framework for nonlinear filtering, which enables to optimally process real signals without predefined statistical models. An application to biomedical imaging, in which the acquisition instruments are based on photon counters, is demonstrated; we propose to incorporate the temporal information, which is commonly ignored in existing methods, for image enhancement.
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empirical Intrinsic Geometry for nonlinear modeling and time series filtering
Proceedings of the National Academy of Sciences of the United States of America, 2013Co-Authors: Ronen Talmon, Ronald R CoifmanAbstract:In this paper, we present a method for time series analysis based on empirical Intrinsic Geometry (EIG). EIG enables one to reveal the low-dimensional parametric manifold as well as to infer the underlying dynamics of high-dimensional time series. By incorporating concepts of information Geometry, this method extends existing geometric analysis tools to support stochastic settings and parametrizes the Geometry of empirical distributions. However, the statistical models are not required as priors; hence, EIG may be applied to a wide range of real signals without existing definitive models. We show that the inferred model is noise-resilient and invariant under different observation and instrumental modalities. In addition, we show that it can be extended efficiently to newly acquired measurements in a sequential manner. These two advantages enable us to revisit the Bayesian approach and incorporate empirical dynamics and Intrinsic Geometry into a nonlinear filtering framework. We show applications to nonlinear and non-Gaussian tracking problems as well as to acoustic signal localization.
Wenwu Zhu - One of the best experts on this subject based on the ideXlab platform.
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kernel clustering on symmetric positive definite manifolds via double approximated low rank representation
International Conference on Multimedia and Expo, 2020Co-Authors: Xinglin Piao, Junbin Gao, Yanfeng Sun, Xin Yang, Baocai Yin, Wenwu ZhuAbstract:As an effective descriptor, Symmetric Positive Definite (SPD) matrix is widely used in several areas such as image clustering. Recently, researchers proposed some effective methods based on low rank theory for SPD data clustering with nonlinear metric. However, single nuclear norm is always adopted to formulate the low rank model in these methods, which would lead to suboptimal solution. In this paper, we proposed a novel double low rank representation method for SPD clustering problem, in which matrix factorization and nonconvex rank constraint are combined to reveal the Intrinsic property of the data instead of employing the nuclear norm. Meanwhile, kernel method and Log-Euclidean metric are combined to better explore the Intrinsic Geometry within SPD data. The proposed method has been evaluated on several public datasets and the experimental results demonstrate that the proposed method outperforms the state-of-the-art ones.
Tie Qiu - One of the best experts on this subject based on the ideXlab platform.
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a fast ellipse detector using projective invariant pruning
IEEE Transactions on Image Processing, 2017Co-Authors: Qi Jia, Xin Fan, Zhongxuan Luo, Lianbo Song, Tie QiuAbstract:Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis, where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy. The algorithm leverages a newly developed projective invariant to significantly prune the undesired candidates and to pick out elliptical ones. The invariant is able to reflect the Intrinsic Geometry of a planar curve, giving the value of −1 on any three collinear points and +1 for any six points on an ellipse. Thus, we apply the pruning and picking by simply comparing these binary values. Moreover, the calculation of the invariant only involves the determinant of a $3\times 3$ matrix. Extensive experiments on three challenging data sets with 648 images demonstrate that our detector runs 20%–50% faster than the state-of-the-art algorithms with the comparable or higher precision.
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a fast ellipse detector using projective invariant pruning
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Qi Jia, Xin Fan, Zhongxuan Luo, Lianbo Song, Tie QiuAbstract:Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy. The algorithm leverage a newly developed projective invariant to significantly prune the undesired candidates and to pick out elliptical ones. The invariant is able to reflect the Intrinsic Geometry of a planar curve, giving the value of -1 on any three collinear points and +1 for any six points on an ellipse. Thus, we apply the pruning and picking by simply comparing these binary values. Moreover, the calculation of the invariant only involves the determinant of a 3*3 matrix. Extensive experiments on three challenging data sets with 650 images demonstrate that our detector runs 20%-50% faster than the state-of-the-art algorithms with the comparable or higher precision.
