Ellipsoidal Subspace Support Vector Data Description

Multimodal Subspace Support Vector Data Description

In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact description of data coming from all the modalities. For data in each modality, we define a separate transformation to map the data from the corresponding feature space to the new optimized subspace by exploiting the available information from the class of interest only. We also propose dif…

图嵌入子空间支持向量数据描述

Graph-Embedded Subspace Support Vector Data Description

本研究提出一种基于图嵌入的新型单类分类子空间学习框架，涵盖现有子空间单类技术，提供谱解法等替代方案，结合支持向量数据描述构建新方法，实验验证其性能优于基线与现有方法。

基于牛顿法的子空间支持向量数据描述

Newton Method-based Subspace Support Vector Data Description

针对S-SVDD依赖一阶梯度下降易陷入次优的问题，本文引入牛顿法并给出线性与非线性优化形式，实验表明其在多数情况下性能更优。

Explaining Anomalies in Groups with Characterizing Subspace Rules

Anomaly detection has numerous applications and has been studied vastly. We consider a complementary problem that has a much sparser literature: anomaly description. Interpretation of anomalies is crucial for practitioners for sense-making, troubleshooting, and planning actions. To this end, we present a new approach called x-PACS (for eXplaining Patterns of Anomalies with Characterizing Subspaces), which "reverse-engineers" the known anomalies by identifying (1) the groups (or patterns) that they form, and (2) the characterizing subspace and feature rules that separate each anomalous pattern …

基于图正则化的多模态子空间学习的智能电网异常检测

Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach

本文提出一种结合图正则化的多模态子空间学习方法，用于智能电网异常检测，通过共享低维子空间提升鲁棒性，验证了图先验与多模态子空间学习结合的有效性。

Subspace Regularizers for Few-Shot Class Incremental Learning

Few-shot class incremental learning -- the problem of updating a trained classifier to discriminate among an expanded set of classes with limited labeled data -- is a key challenge for machine learning systems deployed in non-stationary environments. Existing approaches to the problem rely on complex model architectures and training procedures that are difficult to tune and re-use. In this paper, we present an extremely simple approach that enables the use of ordinary logistic regression classifiers for few-shot incremental learning. The key to this approach is a new family of subspace regular…