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Caijuan Shi - One of the best experts on this subject based on the ideXlab platform.

  • Hessian Semi-Supervised Sparse Feature Selection Based on ${L_{2,1/2}}$ -Matrix Norm
    IEEE Transactions on Multimedia, 2015
    Co-Authors: Caijuan Shi, Qiuiqi Ruan, Ruizhen Zhao
    Abstract:

    Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- Matrix Norm, namely Hessian sparse feature selection based on L2,1/2- Matrix Norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-Matrix Norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications.

  • hessian semi supervised sparse feature selection based on l_ 2 1 2 Matrix Norm
    IEEE Transactions on Multimedia, 2015
    Co-Authors: Caijuan Shi, Qiuiqi Ruan, Ruizhen Zhao
    Abstract:

    Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- Matrix Norm, namely Hessian sparse feature selection based on L2,1/2- Matrix Norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-Matrix Norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications.

  • Sparse feature selection based on L2,1/2-Matrix Norm for web image annotation
    Neurocomputing, 2015
    Co-Authors: Caijuan Shi, Qiuqi Ruan, Song Guo, Yi Tian
    Abstract:

    Abstract Web image annotation based on sparse feature selection has received an increasing amount of interest in recent years. However, existing sparse feature selection methods become less effective and efficient. This raises an urgent need to develop good sparse feature selection methods to improve web image annotation performance. In this paper we propose a novel sparse feature selection framework for web image annotation, namely Sparse Feature Selection based on L2,1/2-Matrix Norm (SFSL). SFSL can select more sparse and more discriminative features by exploiting the l2,1/2-Matrix Norm with shared subspace learning, and then improve the web image annotation performance. We proposed an efficient iterative algorithm to optimize the objective function. Extensive experiments are performed on two web image datasets. The experimental results have validated that our method outperforms the state-of-the-art algorithms and suits for large-scale web image annotation.

Ruizhen Zhao - One of the best experts on this subject based on the ideXlab platform.

  • Hessian Semi-Supervised Sparse Feature Selection Based on ${L_{2,1/2}}$ -Matrix Norm
    IEEE Transactions on Multimedia, 2015
    Co-Authors: Caijuan Shi, Qiuiqi Ruan, Ruizhen Zhao
    Abstract:

    Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- Matrix Norm, namely Hessian sparse feature selection based on L2,1/2- Matrix Norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-Matrix Norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications.

  • hessian semi supervised sparse feature selection based on l_ 2 1 2 Matrix Norm
    IEEE Transactions on Multimedia, 2015
    Co-Authors: Caijuan Shi, Qiuiqi Ruan, Ruizhen Zhao
    Abstract:

    Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- Matrix Norm, namely Hessian sparse feature selection based on L2,1/2- Matrix Norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-Matrix Norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications.

Qiuiqi Ruan - One of the best experts on this subject based on the ideXlab platform.

  • Hessian Semi-Supervised Sparse Feature Selection Based on ${L_{2,1/2}}$ -Matrix Norm
    IEEE Transactions on Multimedia, 2015
    Co-Authors: Caijuan Shi, Qiuiqi Ruan, Ruizhen Zhao
    Abstract:

    Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- Matrix Norm, namely Hessian sparse feature selection based on L2,1/2- Matrix Norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-Matrix Norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications.

  • hessian semi supervised sparse feature selection based on l_ 2 1 2 Matrix Norm
    IEEE Transactions on Multimedia, 2015
    Co-Authors: Caijuan Shi, Qiuiqi Ruan, Ruizhen Zhao
    Abstract:

    Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- Matrix Norm, namely Hessian sparse feature selection based on L2,1/2- Matrix Norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-Matrix Norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications.

Yi Tian - One of the best experts on this subject based on the ideXlab platform.

  • Sparse feature selection based on L2,1/2-Matrix Norm for web image annotation
    Neurocomputing, 2015
    Co-Authors: Caijuan Shi, Qiuqi Ruan, Song Guo, Yi Tian
    Abstract:

    Abstract Web image annotation based on sparse feature selection has received an increasing amount of interest in recent years. However, existing sparse feature selection methods become less effective and efficient. This raises an urgent need to develop good sparse feature selection methods to improve web image annotation performance. In this paper we propose a novel sparse feature selection framework for web image annotation, namely Sparse Feature Selection based on L2,1/2-Matrix Norm (SFSL). SFSL can select more sparse and more discriminative features by exploiting the l2,1/2-Matrix Norm with shared subspace learning, and then improve the web image annotation performance. We proposed an efficient iterative algorithm to optimize the objective function. Extensive experiments are performed on two web image datasets. The experimental results have validated that our method outperforms the state-of-the-art algorithms and suits for large-scale web image annotation.

David V. Anderson - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - Using Block Coordinate Descent to Learn Sparse Coding Dictionaries with a Matrix Norm Update
    2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018
    Co-Authors: Bradley M. Whitaker, David V. Anderson
    Abstract:

    Researchers have recently examined a modified approach to sparse coding that encourages dictionaries to learn anomalous features. This is done by incorporating the Matrix I-Norm, or $\ell_{1,\infty}$ mixed Matrix Norm, into the dictionary update portion of a sparse coding algorithm. However, solving a Matrix Norm minimization problem in each iteration of the algorithm causes it to run more slowly. The purpose of this paper is to introduce block coordinate descent, a subgradient-like approach to minimizing the Matrix Norm, to the dictionary update. This approach removes the need to solve a convex optimization program in each iteration and dramatically reduces the time required to learn a dictionary. Importantly, the dictionary learned in this manner can still model anomalous features present in a dataset.

  • SP/SPE - Learning anomalous features via sparse coding using Matrix Norms
    2015 IEEE Signal Processing and Signal Processing Education Workshop (SP SPE), 2015
    Co-Authors: Bradley M. Whitaker, David V. Anderson
    Abstract:

    Our goal is to find anomalous features in a dataset using the sparse coding concept of dictionary learning. Rather than using the averaged column l2-Norm for the dictionary update as is typically done in sparse coding, we explore using three Matrix Norms: ∥·∥1, ∥·∥2, and ∥·∥∞. Minimizing the Matrix Norms represents minimizing a maximum deviation in the reconstruction error rather than an average deviation, hopefully allowing us to find features that contribute significantly but infrequently to sample training points. We find that while solving for the dictionaries using Matrix Norm minimization takes longer to compute, all three methods are able to recover a known basis from a simple set of training data. In addition, the ∥·∥1 Matrix Norm is able to recover a known anomalous feature in the training data that the other Norms (including the standard averaged l2-Norm) are unable to find.