Abstract Similar to binary and multi-class classifiers, one-class classifiers have to face the difficulty of ’curse of dimensionality’ when they are applied to deal with high-dimensional samples. As an efficient dimensionality reduction method, sparse coding tries to learn a set of over-complete bases to represent the given samples. It can effectively overcome the ’curse of dimensionality’ problem. However, the traditional sparse coding only fit for tackling Gaussian noise. When the noise within the given set of samples obey non-Gaussian distribution, the conventional sparse coding cannot obtain accurate coefficient vectors. To make sparse coding more fit for dealing with non-Gaussian noise and enhance the sparseness of the obtained coefficient vectors, correntropy is utilized to substitute its reconstruction error term and logarithmic penalty function is introduced as its regularization term. Furthermore, the obtained sparse coefficient vectors are used as the input vectors for one-class support vector machine (OCSVM). Experimental results on twenty UCI benchmark data sets and one handwritten digit data set demonstrate that the proposed method achieves better anti-noise and generalization abilities in comparison with its related approaches.

中文翻译：

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基于相关熵和对数惩罚函数的一类分类鲁棒稀疏编码

摘要 与二分类器和多类分类器类似，一类分类器在处理高维样本时也面临着“维数诅咒”的难题。作为一种有效的降维方法，稀疏编码试图学习一组过完备的基来表示给定的样本。它可以有效地克服“维度诅咒”问题。然而，传统的稀疏编码只适合处理高斯噪声。当给定样本集内的噪声服从非高斯分布时，传统的稀疏编码无法获得准确的系数向量。为了使稀疏编码更适合处理非高斯噪声并增强获得的系数向量的稀疏性，利用相关熵代替其重构误差项，引入对数惩罚函数作为其正则化项。此外，获得的稀疏系数向量用作一类支持向量机（OCSVM）的输入向量。在 20 个 UCI 基准数据集和 1 个手写数字数据集上的实验结果表明，与相关方法相比，所提出的方法具有更好的抗噪和泛化能力。