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Sparse Representation and Its Applications
Yuanqing Li
Center for Brain Computer Interface &
Brain Information Processing
South China University of Technology,
Guangzhou, China
-Yuanqing Li
Center for Brain Computer Interface &
Bra
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基于峰值变换的信号稀疏表示及重建，相关理论说明。-Sparse representation of signals based on peak transform and reconstruction, and related theoretical explanations.
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含噪信号的稀疏表示研究，该方法的计算复杂度较低-Sparse representation of noisy signals, the low complexity of the method of calculation
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基于稀疏表示的逆合成孔径雷达自聚焦成像，讲述了自聚焦方法-Based on sparse representation inverse synthetic aperture radar imaging self-focusing
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this tutorial is about signal classification using sparse representation.
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Kernel Reconstruction ICA for Sparse Representation
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Kernel Reconstruction ICA for Sparse Representation
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基于Gabor特征和字典学习的高斯混合稀疏表示图像识别-Image recognition based on Gabor features and dictionary learning of Gauss hybrid sparse representation
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为了解决基于去噪重建过程，即解决稀疏系数原理的图像稀疏表示-In order to solve the reconstruction process based de-noising, that a solution to the sparse coefficient principle sparse representation
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joint sparse representation
(JSR)方法用于车内语音增强的特征降噪算法-address reducing the mismatch between training
and testing conditions for hands-free in-car speech recognition. It
is well known that the distortions caused by background noise,
channel effec
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