稀疏表征(Sparse Representation)作为一种有效的信号表征手段,近年来一直是热门的研究课题。信号的稀疏性给信号处理带来很大的方便。
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稀疏表征分类 Gabor Sparse Representation Classification
Sparse component analysis (SCA) is a method of signal processing based on sparse representation.
一般的信号在时域中并不是稀疏的，因此在很多应用中需要寻找有效的稀疏表征来进行信号处理。
参考来源 - 欠定混叠语音信号盲分离方法研究In order to maximize the robustness to occlusion, we introduce transverse partition scheme, apply random projection and sparse representation to each of the blocks and aggregate the results by mean value of the discrimination factor.
同时,为了达到对于遮掩的极大鲁棒性,采取图像横向四等分策略,在各等分块上分别进行随机投影和稀疏表征,以四块平均判据值进行分类识别。
参考来源 - 基于随机投影和稀疏表征的红外人脸识别方法·2,447,543篇论文数据，部分数据来源于NoteExpress
稀疏表征理论在模式识别中的应用引起广泛的关注。
Very recently, the sparse representation theory in pattern recognition arouses widespread concern.
通过对八面河油田井网密集区及井网稀疏的欠开发区的微构造研究，探讨了微构造表征技术。
Characterization technique of microstructure is discussed based on the microstructure study of the dense well spacing area and under-exploited area with wide-spaced Wells.
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