基于局部主成分分析的稀疏表示分类算法改进
Improving Sparse Representation-Based Classification Using Local Principal Component Analysis
摘要：
Wright等人提出的基于稀疏表示的分类（SRC），在训练样本的字典上寻求测试样本的稀疏分解，并分类到最有贡献的类。因为它假设测试样本可以被写为其同类训练样本的线性组合，所以SRC的成功取决于训练集的大小和代表性。我们提出的分类算法扩大了训练集，通过使用局部主成分分析近似的切超平面的类流形在每个训练样本的基向量。SRC中的字典由适应测试样本的本地字典代替，并且包括训练样本及其对应的切基向量。我们使用一个合成数据集和三个人脸数据库来证明，该方法可以实现更高的分类精度比SRC的情况下，稀疏采样，非线性类流形，严格的降维。
英文摘要：
Sparse representation-based classification (SRC), proposed by Wright et al., seeks the sparsest decomposition of a test sample over the dictionary of training samples, with classification to the most-contributing class. Because it assumes test samples can be written as linear combinations of their same-class training samples, the success of SRC depends on the size and representativeness of the training set. Our proposed classification algorithm enlarges the training set by using local principal component analysis to approximate the basis vectors of the tangent hyperplane of the class manifold at each training sample. The dictionary in SRC is replaced by a local dictionary that adapts to the test sample and includes training samples and their corresponding tangent basis vectors. We use a synthetic data set and three face databases to demonstrate that this method can achieve higher classification accuracy than SRC in cases of sparse sampling, nonlinear class manifolds, and stringent dimension reduction.
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