The Experts below are selected from a list of 22119 Experts worldwide ranked by ideXlab platform
Jian Yang - One of the best experts on this subject based on the ideXlab platform.
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Discriminative Feature representation for Noisy image quality assessment
Multimedia Tools and Applications, 2020Co-Authors: Hui Tang, Yang Chen, Zhiping Wang, Lu Zhang, Jian Yang, Huazhong Shu, Limin Luo, Gouenou CoatrieuxAbstract:Blind image quality assessment (BIQA) is one of the most challenging and difficult tasks in the field of IQA. Given that sparse representation through dictionary learning can learn the image Feature well, this paper proposed a method termed Discriminative Feature Representation (DFR) from the perspective of Feature learning for noise contaminated image quality assessment. DFR makes use of two sub-dictionaries composed of atoms featuring desirable image structures and undesirable noise, respectively. Noise is quantified via a joint evaluation of the sparse coefficients related to the atoms in the two sub-dictionaries. The method is validated using public databases with different types of noise, a comparison with other up-to-date methods is provided. The proposed method is also applied to CT images acquired at different-level doses and reconstructed by various well-known algorithms.
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Discriminative Feature Representation to Improve Projection Data Inconsistency for Low Dose CT Imaging
IEEE transactions on medical imaging, 2017Co-Authors: Jin Liu, Yang Chen, Jian Yang, Huazhong Shu, Limin Luo, Gouenou Coatrieux, Yi Zhang, Wei Yang, Qianjin FengAbstract:In low dose computed tomography (LDCT) imaging, the data inconsistency of measured noisy projections can significantly deteriorate reconstruction images. To deal with this problem, we propose here a new sinogram restoration approach, the sinogram- Discriminative Feature representation (S-DFR) method. Different from other sinogram restoration methods, the proposed method works through a 3-D representation-based Feature decomposition of the projected attenuation component and the noise component using a well-designed composite dictionary containing atoms with Discriminative Features. This method can be easily implemented with good robustness in parameter setting. Its comparison to other competing methods through experiments on simulated and real data demonstrated that the S-DFR method offers a sound alternative in LDCT.
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Sparse Discriminative Feature weights learning
Neurocomputing, 2016Co-Authors: Hui Yan, Jian YangAbstract:Sparse representation, a locality-based data representation method, leads to promising results in many scientific and engineering fields. Meanwhile in the study of Feature selection, locality preserving is widely recognized as an effective measurement criterion. In this paper, we introduce l1-norm driven sparse representation into Feature selection, and propose a novel joint Feature weights learning algorithm, named sparse Discriminative Feature weights (SDFW). SDFW assigns the highest score to the Feature that has the smallest difference between within-class reconstruction residual and between-class reconstruction residual in the space of selected Features. It possesses the following advantages: (1) compared with Feature selection methods based on k nearest neighbors, SDFW automatically (vs. manually) determines neighborhood for individual sample; (2) compared with conventional heuristic Feature search which selects Features individually, SDFW selects Feature subset in batch mode. Extensive experiments on different data types demonstrate the effectiveness of SDFW.
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Sparse Discriminative Feature selection
Pattern Recognition, 2015Co-Authors: Hui Yan, Jian YangAbstract:As sparse representation-based classifier (SRC) is developed, it has drawn more and more attentions in dimension reduction. In this paper, we introduce SRC based measurement criterion into Feature selection, and then propose a novel method called sparse Discriminative Feature selection. Our objective function aims to find a subset of Features, which minimize the within-class reconstruction residual and simultaneously maximize the between-class reconstruction residual in the subspace of selected Features. A greedy algorithm and a joint selection algorithm are devised to efficiently solve the proposed combinatorial optimization formulation. In particular, our joint selection algorithm adds l 2 , 1 - norm minimization into the objective function, which reduces the redundant and learns Features weights simultaneously. A new iterative algorithm is also developed to optimize the proposed objective function. Experiments on benchmark data sets demonstrate the performance of our Feature selection method. HighlightsThe proposed method selects Features that can preserve the sparse reconstructive relationship of the data.A greedy algorithm and a joint selection algorithm are devised to efficiently solve the proposed formulation.We incorporate Discriminative analysis and l2;1_norm minimization into a joint Feature selection.
Feiping Nie - One of the best experts on this subject based on the ideXlab platform.
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Subspace Sparse Discriminative Feature Selection.
IEEE transactions on cybernetics, 2020Co-Authors: Feiping Nie, Zheng Wang, Lai Tian, Rong WangAbstract:In this article, we propose a novel Feature selection approach via explicitly addressing the long-standing subspace sparsity issue. Leveraging l2,1-norm regularization for Feature selection is the major strategy in existing methods, which, however, confronts sparsity limitation and parameter-tuning trouble. To circumvent this problem, employing the l2,0-norm constraint to improve the sparsity of the model has gained more attention recently whereas, optimizing the subspace sparsity constraint is still an unsolved problem, which only can acquire an approximate solution and without convergence proof. To address the above challenges, we innovatively propose a novel subspace sparsity Discriminative Feature selection (S²DFS) method which leverages a subspace sparsity constraint to avoid tuning parameters. In addition, the trace ratio formulated objective function extremely ensures the discriminability of selected Features. Most important, an efficient iterative optimization algorithm is presented to explicitly solve the proposed problem with a closed-form solution and strict convergence proof. To the best of our knowledge, such an optimization algorithm of solving the subspace sparsity issue is first proposed in this article, and a general formulation of the optimization algorithm is provided for improving the extensibility and portability of our method. Extensive experiments conducted on several high-dimensional text and image datasets demonstrate that the proposed method outperforms related state-of-the-art methods in pattern classification and image retrieval tasks.
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IJCAI - Worst-Case Discriminative Feature Selection
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019Co-Authors: Shuangli Liao, Quanxue Gao, Feiping Nie, Yang Liu, Xiangdong ZhangAbstract:Feature selection plays a critical role in data mining, driven by increasing Feature dimensionality in target problems. In this paper, we propose a new criterion for Discriminative Feature selection, worst-case Discriminative Feature selection (WDFS). Unlike Fisher Score and other methods based on the Discriminative criteria considering the overall (or average) separation of data, WDFS adopts a new perspective called worst-case view which arguably is more suitable for classification applications. Specifically, WDFS directly maximizes the ratio of the minimum of between-class variance of all class pairs over the maximum of within-class variance, and thus it duly considers the separation of all classes. Otherwise, we take a greedy strategy by finding one Feature at a time, but it is very easy to implement. Moreover, we also utilize the correlation between Features to help reduce the redundancy and extend WDFS to uncorrelated WDFS (UWDFS). To evaluate the effectiveness of the proposed algorithm, we conduct classification experiments on many real data sets. In the experiment, we respectively use the original Features and the score vectors of Features over all class pairs to calculate the correlation coefficients, and analyze the experimental results in these two ways. Experimental results demonstrate the effectiveness of WDFS and UWDFS.
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Self-weighted Discriminative Feature selection via adaptive redundancy minimization
Neurocomputing, 2018Co-Authors: Yicang Zhou, Rui Zhang, Yanni Xiao, Feiping NieAbstract:Abstract In this paper, a novel self-weighted orthogonal linear discriminant analysis (SOLDA) method is firstly proposed, such that optimal weight can be automatically achieved to balance both between-class and within-class scatter matrices. Since correlated Features tend to have similar rankings, multiple adopted criteria might lead to the state that top ranked Features are selected with large correlations, such that redundant information is brought about. To minimize associated redundancy, an original regularization term is introduced to the proposed SOLDA problem to penalize the high-correlated Features. Different from other methods and techniques, we optimize redundancy matrix as a variable instead of setting it as a priori, such that correlations among all the Features can be adaptively evaluated. Additionally, a brand new recursive method is derived to achieve the selection matrix heuristically, such that closed form solution can be obtained with holding the orthogonality. Consequently, self-weighted Discriminative Feature selection via adaptive redundancy minimization (SDFS-ARM) method can be summarized, such that non-redundant Discriminative Features could be selected correspondingly. Eventually, the effectiveness of the proposed SDFS-ARM method is further validated by the empirical results.
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Self-Weighted Supervised Discriminative Feature Selection
IEEE transactions on neural networks and learning systems, 2017Co-Authors: Rui Zhang, Feiping NieAbstract:In this brief, a novel self-weighted orthogonal linear discriminant analysis (SOLDA) problem is proposed, and a self-weighted supervised Discriminative Feature selection (SSD-FS) method is derived by introducing sparsity-inducing regularization to the proposed SOLDA problem. By using the row-sparse projection, the proposed SSD-FS method is superior to multiple sparse Feature selection approaches, which can overly suppress the nonzero rows such that the associated Features are insufficient for selection. More specifically, the orthogonal constraint ensures the minimal number of selectable Features for the proposed SSD-FS method. In addition, the proposed Feature selection method is able to harness the discriminant power such that the Discriminative Features are selected. Consequently, the effectiveness of the proposed SSD-FS method is validated theoretically and experimentally.
Hui Yan - One of the best experts on this subject based on the ideXlab platform.
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Sparse Discriminative Feature weights learning
Neurocomputing, 2016Co-Authors: Hui Yan, Jian YangAbstract:Sparse representation, a locality-based data representation method, leads to promising results in many scientific and engineering fields. Meanwhile in the study of Feature selection, locality preserving is widely recognized as an effective measurement criterion. In this paper, we introduce l1-norm driven sparse representation into Feature selection, and propose a novel joint Feature weights learning algorithm, named sparse Discriminative Feature weights (SDFW). SDFW assigns the highest score to the Feature that has the smallest difference between within-class reconstruction residual and between-class reconstruction residual in the space of selected Features. It possesses the following advantages: (1) compared with Feature selection methods based on k nearest neighbors, SDFW automatically (vs. manually) determines neighborhood for individual sample; (2) compared with conventional heuristic Feature search which selects Features individually, SDFW selects Feature subset in batch mode. Extensive experiments on different data types demonstrate the effectiveness of SDFW.
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Sparse Discriminative Feature selection
Pattern Recognition, 2015Co-Authors: Hui Yan, Jian YangAbstract:As sparse representation-based classifier (SRC) is developed, it has drawn more and more attentions in dimension reduction. In this paper, we introduce SRC based measurement criterion into Feature selection, and then propose a novel method called sparse Discriminative Feature selection. Our objective function aims to find a subset of Features, which minimize the within-class reconstruction residual and simultaneously maximize the between-class reconstruction residual in the subspace of selected Features. A greedy algorithm and a joint selection algorithm are devised to efficiently solve the proposed combinatorial optimization formulation. In particular, our joint selection algorithm adds l 2 , 1 - norm minimization into the objective function, which reduces the redundant and learns Features weights simultaneously. A new iterative algorithm is also developed to optimize the proposed objective function. Experiments on benchmark data sets demonstrate the performance of our Feature selection method. HighlightsThe proposed method selects Features that can preserve the sparse reconstructive relationship of the data.A greedy algorithm and a joint selection algorithm are devised to efficiently solve the proposed formulation.We incorporate Discriminative analysis and l2;1_norm minimization into a joint Feature selection.
Boyuan Jiang - One of the best experts on this subject based on the ideXlab platform.
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AAAI - Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation
Proceedings of the AAAI Conference on Artificial Intelligence, 2019Co-Authors: Chao Chen, Zhihong Chen, Boyuan JiangAbstract:Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared Feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domain alignment approaches can only reduce, but not remove the domain shift, target domain samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the hyperplane learned from the source domain. To alleviate this issue, we propose to joint domain alignment and Discriminative Feature learning, which could benefit both domain alignment and final classification. Specifically, an instance-based Discriminative Feature learning method and a center-based Discriminative Feature learning method are proposed, both of which guarantee the domain invariant Features with better intra-class compactness and inter-class separability. Extensive experiments show that learning the Discriminative Features in the shared Feature space can significantly boost the performance of deep domain adaptation methods.
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joint domain alignment and Discriminative Feature learning for unsupervised deep domain adaptation
National Conference on Artificial Intelligence, 2019Co-Authors: Chao Chen, Zhihong Chen, Boyuan JiangAbstract:Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared Feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domain alignment approaches can only reduce, but not remove the domain shift, target domain samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the hyperplane learned from the source domain. To alleviate this issue, we propose to joint domain alignment and Discriminative Feature learning, which could benefit both domain alignment and final classification. Specifically, an instance-based Discriminative Feature learning method and a center-based Discriminative Feature learning method are proposed, both of which guarantee the domain invariant Features with better intra-class compactness and inter-class separability. Extensive experiments show that learning the Discriminative Features in the shared Feature space can significantly boost the performance of deep domain adaptation methods.
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joint domain alignment and Discriminative Feature learning for unsupervised deep domain adaptation
National Conference on Artificial Intelligence, 2019Co-Authors: Chao Chen, Zhihong Chen, Boyuan JiangAbstract:Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared Feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domain alignment approaches can only reduce, but not remove the domain shift, target domain samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the hyperplane learned from the source domain. To alleviate this issue, we propose to joint domain alignment and Discriminative Feature learning, which could benefit both domain alignment and final classification. Specifically, an instance-based Discriminative Feature learning method and a center-based Discriminative Feature learning method are proposed, both of which guarantee the domain invariant Features with better intra-class compactness and inter-class separability. Extensive experiments show that learning the Discriminative Features in the shared Feature space can significantly boost the performance of deep domain adaptation methods.
Ángel De La Torre - One of the best experts on this subject based on the ideXlab platform.
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Discriminative Feature Selection for Automatic Classification of Volcano-Seismic Signals
IEEE Geoscience and Remote Sensing Letters, 2012Co-Authors: Isaac Alvarez, Carmen Benitez, Luz García, Guillermo Cortés, Ángel De La TorreAbstract:Feature extraction is a critical element in automatic pattern classification. In this letter, we propose different sets of parameters for classification of volcano-seismic signals, and the Discriminative Feature selection (DFS) method is applied for selecting the minimum number of Features containing most of the Discriminative information. We have applied DFS to a conventional cepstral-based parameterization (with 39 Features) and to an extended set of parameters (including 84 Features). Classification experiments using seismograms recorded at Colima Volcano (Mexico) show that, for the most complex classifier and using the cepstral-based parameterization, DFS provided a reduction of the error rate from 24.3% (using 39 Features) to 15.5% (ten components). When DFS is applied to the extended parameterization, the error rate decreased from 27.9% (84 Features) to 13.8% (14 Features). These results show the utility of DFS for identifying the best components from the original Feature vector and for exploring new parameterizations for the classification of volcano-seismic signals.
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Discriminative Feature weighting for HMM-based continuous speech recognizers
Speech Communication, 2001Co-Authors: Ángel De La Torre, Antonio M. Peinado, Antonio J. Rubio, José C. Segura, Carmen BenitezAbstract:The Discriminative Feature Extraction (DFE) method provides an appropriate formalism for the design of the front-end Feature extraction module in pattern classification systems. In the recent years, this formalism has been successfully applied to different speech recognition problems, like classification of vowels, classification of phonemes or isolated word recognition. The DFE formalism can be applied to weight the contribution of the components in the Feature vector. This variant of DFE, that we call Discriminative Feature Weighting (DFW), improves the pattern classification systems by enhancing those components more relevant for the discrimination among the different classes. This paper is dedicated to the application of the DFW formalism to Continuous Speech Recognizers (CSR) based on Hidden Markov Models (HMMs). Two different types of HMM-based speech recognizers are considered: recognizers based on Discrete-HMMs (DHMMs) (for which the acoustic evaluation is based on an Euclidean distance measure) and Semi-Continuous-HMMs (SCHMMs) (for which the acoustic evaluation is performed making use of a mixture of multivariated Gaussians). We report how the components can be weighted and how the weights can be Discriminatively trained and applied to the speech recognizers. We present recognition results for several continuous speech recognition tasks. The experimental results show the utility of DFW for HMM-based continuous speech recognizers.
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EUROSPEECH - Discriminative Feature extraction for speech recognition in noise.
1997Co-Authors: Ángel De La Torre, Antonio M. Peinado, Antonio J. Rubio, Pedro García-teodoroAbstract:Signal representation is crucial for designinga speechrecognizer. The Feature extractor selects the information to be used by the classifier to perform the recognition. In noisy environments, the data vectors representing the speech signal are changed and the recognizer performance is degraded by two main facts: (1) the mismatch between the training and the recognition conditions and (2) the degradation of the signal to be recognized. In such a situation, the representation of the speech signal plays an important role. In this paper, we analyze the importance of the representation for speechrecognition in noise. We apply the Discriminative Feature Extraction (DFE) method to optimize the representation. The experiments presented in this work show that the DFE method, which has been successfully applied in clean environments, leads also to improvements of the speech recognizers in noise.