Unsupervised Classification of Array Data Based on the L1-Norm
Unsupervised Classification of Array Data Based on the L1-Norm
复制标题
基于L1范数的数组数据无监督分类
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
V. Zarzoso
中科院分区：
文献类型：
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作者：
R. Martín;V. Zarzoso
在过去的十年里，L1范数准则一直是信号处理和机器学习领域的一系列研究的主题，特别是由于它们能够利用潜在变量的稀疏性以及在存在错误数据的情况下的鲁棒性。在这些标准中，L1范数主成分分析（L1-PCA）引起了相当大的关注，导致了各种优化算法和与其他数据处理技术，如独立成分分析的连接。目前的贡献采取了一个步骤，在L1-PCA的表征，探索其线性判别能力。针对无监督分类问题，提出了一种基于L2范数约束的L1范数最大化的L1-PCA算法。提出的L1-PCA变体的歧视性质证明通过一些计算机实验。
L1-norm criteria have been the subject a flurry of research in signal processing and machine learning over the last decade, especially due to their ability to exploit the sparsity of latent variables and their robustness in the presence of faulty data. Among such criteria, L1-norm principal component analysis (L1-PCA) has drawn considerable attention, resulting in a variety of optimization algorithms and connections with other data processing techniques such as independent component analysis. The present contribution takes a step forward in the characterization of L1-PCA by exploring its linear discrimination capabilities. A variant of L1-PCA consisting of L1-norm maximization subject to an L2-norm constraint is put forward for unsupervised classification. The discrimination properties of the proposed L1-PCA variant are demonstrated through a number of computer experiments.