Outlier detection toward high-dimensional industrial data using extreme tensor-train learning machine with compression

Abstract

Outlier detection in a high-dimensional dataset is a significant but challenging task in a number of applications. Extreme learning machine (ELM) is a powerful modeling tool for identifying outlier in an underlying dataset. However, when dealing with outliers in high-dimensional industry data, ELM brings huge storage and computational cost. To address this issue, we propose ELM based on a tensor-train format (ETFLM). Specifically, a tensor-train layer is builded with tensor-train decomposition. The fully connected layers of a neural network are replaced with tensor-train layers. Based on tensor-train layers and ELM, ETFLM is proposed in this study and its training algorithm is further presented. The experimental results show that ETFLM achieves high compression rate on low-dimensional data, and detection accuracy is slightly decreased. However, on high-dimensional data, ETFLM achieves more than 60%, whereas traditional algorithms achieve less than 40%.

Keywords

• High-dimensional industrial data
• Outlier detection
• Tensorized compression
• Extreme tensor-train Learning Machine