Deep kernel learning has been well explored for multi-class classification tasks; however, relatively less work is done for one-class classification (OCC). OCC needs samples from only one class to train the model. Most recently, kernel regularized least squares (KRL) method-based deep architecture is developed for the OCC task. This paper introduces a novel extension of this method by embedding minimum variance information within this architecture. This embedding improves the generalization capability of the classifier by reducing the intra-class variance. In contrast to traditional deep learning methods, this method can effectively work with small-size datasets. We conduct a comprehensive set of experiments on 18 benchmark datasets (13 biomedical and 5 other datasets) to demonstrate the performance of the proposed classifier. We compare the results with 16 state-of-the-art one-class classifiers. Further, we also test our method for 2 real-world biomedical datasets viz.; detection of Alzheimer's disease from structural magnetic resonance imaging data and detection of breast cancer from histopathological images. Proposed method exhibits more than 5% F1 score compared to existing state-of-the-art methods for various biomedical benchmark datasets. This makes it viable for application in biomedical fields where relatively less amount of data is available.

中文翻译：

---

一类分类的最小方差嵌入深核正则化最小二乘方法及其在生物医学数据中的应用。

对于多类分类任务，已经深入探索了深度内核学习。但是，针对一类分类（OCC）所做的工作相对较少。OCC仅需要来自一个班级的样本来训练模型。最近，针对OCC任务开发了基于内核正则最小二乘（KRL）方法的深度架构。本文通过将最小方差信息嵌入此体系结构中，介绍了该方法的新颖扩展。该嵌入通过减少类内方差来提高分类器的泛化能力。与传统的深度学习方法相比，该方法可以有效地处理小型数据集。我们对18个基准数据集（13个生物医学和5个其他数据集）进行了全面的实验，以证明所提出分类器的性能。我们将结果与16个最新的一类分类器进行比较。此外，我们还针对2个现实世界的生物医学数据集测试了我们的方法。从结构磁共振成像数据中检测阿尔茨海默氏病，从组织病理学图像中检测乳腺癌。与现有的各种生物医学基准数据集的最新技术相比，该方法的F1得分超过5％。这使其可用于生物医学领域，因为这些领域的数据量相对较少。与现有的各种生物医学基准数据集的最新技术相比，该方法的F1得分超过5％。这使其可用于生物医学领域，因为这些领域的数据量相对较少。与现有的各种生物医学基准数据集的最新技术相比，该方法的F1得分超过5％。这使其可用于生物医学领域，因为这些领域的数据量相对较少。