In parallel basis selection algorithms of sparse signal representation, there will be serious bias in amplitude estimation when frequency is not in grid.
在稀疏信号表示的并行选取字典算法中，当频率不在栅格点上时，对应的幅度估计可能会有很大的偏差。
The recognition problem is taken as one of classifying among multiple linear regression models, and sparse signal representation is used to solve this problem.
将识别问题看作是多个线性回归模型中的分类问题，并用稀疏表示理论解决这些问题。
Signal sparse representation or the optimal N-term approximation is one of the important problems, which is applied to many areas such as the data compression, denoising.
信号的稀疏表示或最佳n -项逼近是数据压缩、噪声抑制等众多应用中的一个重要问题。
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