The Experts below are selected from a list of 22365 Experts worldwide ranked by ideXlab platform

Andrzej Cichocki - One of the best experts on this subject based on the ideXlab platform.

  • frequency recognition in ssvep based bci using multiset canonical correlation analysis
    International Journal of Neural Systems, 2014
    Co-Authors: Yu Zhang, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej Cichocki
    Abstract:

    Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real electro-encephalo-gram (EEG) data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple Linear Transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from 10 healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.

  • frequency recognition in ssvep based bci using multiset canonical correlation analysis
    International Journal of Neural Systems, 2014
    Co-Authors: Yu Zhang, Guoxu Zhou, Xingyu Wang, Andrzej Cichocki
    Abstract:

    Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real electro-encephalo-gram (EEG) data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple Linear Transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from 10 healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.

  • frequency recognition in ssvep based bci using multiset canonical correlation analysis
    arXiv: Machine Learning, 2013
    Co-Authors: Yu Zhang, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej Cichocki
    Abstract:

    Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real EEG data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple Linear Transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from ten healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.

Xilin Chen - One of the best experts on this subject based on the ideXlab platform.

  • multi view discriminant analysis
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
    Co-Authors: Shiguang Shan, Haihong Zhang, Xilin Chen
    Abstract:

    In many computer vision systems, the same object can be observed at varying viewpoints or even by different sensors, which brings in the challenging demand for recognizing objects from distinct even heterogeneous views. In this work we propose a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for multiple views in a non-pairwise manner by jointly learning multiple view-specific Linear Transforms. Specifically, our MvDA is formulated to jointly solve the multiple Linear Transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations from both intra-view and inter-view in the common space. By reformulating this problem as a ratio trace problem, the multiple Linear Transforms are achieved analytically and simultaneously through generalized eigenvalue decomposition. Furthermore, inspired by the observation that different views share similar data structures, a constraint is introduced to enforce the view-consistency of the multiple Linear Transforms. The proposed method is evaluated on three tasks: face recognition across pose, photo versus. sketch face recognition, and visual light image versus near infrared image face recognition on Multi-PIE, CUFSF and HFB databases respectively. Extensive experiments show that our MvDA achieves significant improvements compared with the best known results.

  • multi view discriminant analysis
    European Conference on Computer Vision, 2012
    Co-Authors: Shiguang Shan, Haihong Zhang, Xilin Chen
    Abstract:

    The same object can be observed at different viewpoints or even by different sensors, thus generating multiple distinct even heterogeneous samples. Nowadays, more and more applications need to recognize object from distinct views. Some seminal works have been proposed for object recognition across two views and applied to multiple views in some inefficient pairwise manner. In this paper, we propose a Multi-view Discriminant Analysis (MvDA) method, which seeks for a discriminant common space by jointly learning multiple view-specific Linear Transforms for robust object recognition from multiple views, in a non-pairwise manner. Specifically, our MvDA is formulated to jointly solve the multiple Linear Transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations of the low-dimensional embeddings from both intra-view and inter-view in the common space. By reformulating this problem as a ratio trace problem, an analytical solution can be achieved by using the generalized eigenvalue decomposition. The proposed method is applied to three multi-view face recognition problems: face recognition across poses, photo-sketch face recognition, and Visual (VIS) image vs. Near Infrared (NIR) image face recognition. Evaluations are conducted respectively on Multi-PIE, CUFSF and HFB databases. Intensive experiments show that MvDA can achieve a more discriminant common space, with up to 13% improvement compared with the best known results.

Yuan Yao - One of the best experts on this subject based on the ideXlab platform.

  • boosting with structural sparsity a differential inclusion approach
    Applied and Computational Harmonic Analysis, 2018
    Co-Authors: Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan Yao
    Abstract:

    Abstract Boosting as gradient descent algorithms is one popular method in machine learning. In this paper a novel Boosting-type algorithm is proposed based on restricted gradient descent with structural sparsity control whose underlying dynamics are governed by differential inclusions. In particular, we present an iterative regularization path with structural sparsity where the parameter is sparse under some Linear Transforms, based on variable splitting and the Linearized Bregman Iteration. Hence it is called Split LBI. Despite its simplicity, Split LBI outperforms the popular generalized Lasso in both theory and experiments. A theory of path consistency is presented that equipped with a proper early stopping, Split LBI may achieve model selection consistency under a family of Irrepresentable Conditions which can be weaker than the necessary and sufficient condition for generalized Lasso. Furthermore, some l 2 error bounds are also given at the minimax optimal rates. The utility and benefit of the algorithm are illustrated by several applications including image denoising, partial order ranking of sport teams, and world university grouping with crowdsourced ranking data.

  • boosting with structural sparsity a differential inclusion approach
    arXiv: Machine Learning, 2017
    Co-Authors: Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan Yao
    Abstract:

    Boosting as gradient descent algorithms is one popular method in machine learning. In this paper a novel Boosting-type algorithm is proposed based on restricted gradient descent with structural sparsity control whose underlying dynamics are governed by differential inclusions. In particular, we present an iterative regularization path with structural sparsity where the parameter is sparse under some Linear Transforms, based on variable splitting and the Linearized Bregman Iteration. Hence it is called \emph{Split LBI}. Despite its simplicity, Split LBI outperforms the popular generalized Lasso in both theory and experiments. A theory of path consistency is presented that equipped with a proper early stopping, Split LBI may achieve model selection consistency under a family of Irrepresentable Conditions which can be weaker than the necessary and sufficient condition for generalized Lasso. Furthermore, some $\ell_2$ error bounds are also given at the minimax optimal rates. The utility and benefit of the algorithm are illustrated by several applications including image denoising, partial order ranking of sport teams, and world university grouping with crowdsourced ranking data.

Shiguang Shan - One of the best experts on this subject based on the ideXlab platform.

  • multi view discriminant analysis
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
    Co-Authors: Shiguang Shan, Haihong Zhang, Xilin Chen
    Abstract:

    In many computer vision systems, the same object can be observed at varying viewpoints or even by different sensors, which brings in the challenging demand for recognizing objects from distinct even heterogeneous views. In this work we propose a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for multiple views in a non-pairwise manner by jointly learning multiple view-specific Linear Transforms. Specifically, our MvDA is formulated to jointly solve the multiple Linear Transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations from both intra-view and inter-view in the common space. By reformulating this problem as a ratio trace problem, the multiple Linear Transforms are achieved analytically and simultaneously through generalized eigenvalue decomposition. Furthermore, inspired by the observation that different views share similar data structures, a constraint is introduced to enforce the view-consistency of the multiple Linear Transforms. The proposed method is evaluated on three tasks: face recognition across pose, photo versus. sketch face recognition, and visual light image versus near infrared image face recognition on Multi-PIE, CUFSF and HFB databases respectively. Extensive experiments show that our MvDA achieves significant improvements compared with the best known results.

  • multi view discriminant analysis
    European Conference on Computer Vision, 2012
    Co-Authors: Shiguang Shan, Haihong Zhang, Xilin Chen
    Abstract:

    The same object can be observed at different viewpoints or even by different sensors, thus generating multiple distinct even heterogeneous samples. Nowadays, more and more applications need to recognize object from distinct views. Some seminal works have been proposed for object recognition across two views and applied to multiple views in some inefficient pairwise manner. In this paper, we propose a Multi-view Discriminant Analysis (MvDA) method, which seeks for a discriminant common space by jointly learning multiple view-specific Linear Transforms for robust object recognition from multiple views, in a non-pairwise manner. Specifically, our MvDA is formulated to jointly solve the multiple Linear Transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations of the low-dimensional embeddings from both intra-view and inter-view in the common space. By reformulating this problem as a ratio trace problem, an analytical solution can be achieved by using the generalized eigenvalue decomposition. The proposed method is applied to three multi-view face recognition problems: face recognition across poses, photo-sketch face recognition, and Visual (VIS) image vs. Near Infrared (NIR) image face recognition. Evaluations are conducted respectively on Multi-PIE, CUFSF and HFB databases. Intensive experiments show that MvDA can achieve a more discriminant common space, with up to 13% improvement compared with the best known results.

Yu Zhang - One of the best experts on this subject based on the ideXlab platform.

  • frequency recognition in ssvep based bci using multiset canonical correlation analysis
    International Journal of Neural Systems, 2014
    Co-Authors: Yu Zhang, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej Cichocki
    Abstract:

    Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real electro-encephalo-gram (EEG) data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple Linear Transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from 10 healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.

  • frequency recognition in ssvep based bci using multiset canonical correlation analysis
    International Journal of Neural Systems, 2014
    Co-Authors: Yu Zhang, Guoxu Zhou, Xingyu Wang, Andrzej Cichocki
    Abstract:

    Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real electro-encephalo-gram (EEG) data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple Linear Transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from 10 healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.

  • frequency recognition in ssvep based bci using multiset canonical correlation analysis
    arXiv: Machine Learning, 2013
    Co-Authors: Yu Zhang, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej Cichocki
    Abstract:

    Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real EEG data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple Linear Transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from ten healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.