Online Anomaly Detection via Incremental Tensor Decomposition

Gao, Ming; Zong, Yu; Li, Rui; An, Chengzhi; Duan, Qiang; Yin, Qingshan; Wang, Xiangcheng

doi:10.1007/978-3-030-30952-7_1

Cited by 4 publications

(1 citation statement)

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“…Time series anomaly detection is also a common problem faced by incremental learning. Related research was carried out in [ 119 ], and they used incremental tensor decomposition to solve the task of online time series anomaly detection.…”

Section: Methods Descriptionmentioning

confidence: 99%

An Appraisal of Incremental Learning Methods

Luo

Yin

Bai

et al. 2020

Entropy

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As a special case of machine learning, incremental learning can acquire useful knowledge from incoming data continuously while it does not need to access the original data. It is expected to have the ability of memorization and it is regarded as one of the ultimate goals of artificial intelligence technology. However, incremental learning remains a long term challenge. Modern deep neural network models achieve outstanding performance on stationary data distributions with batch training. This restriction leads to catastrophic forgetting for incremental learning scenarios since the distribution of incoming data is unknown and has a highly different probability from the old data. Therefore, a model must be both plastic to acquire new knowledge and stable to consolidate existing knowledge. This review aims to draw a systematic review of the state of the art of incremental learning methods. Published reports are selected from Web of Science, IEEEXplore, and DBLP databases up to May 2020. Each paper is reviewed according to the types: architectural strategy, regularization strategy and rehearsal and pseudo-rehearsal strategy. We compare and discuss different methods. Moreover, the development trend and research focus are given. It is concluded that incremental learning is still a hot research area and will be for a long period. More attention should be paid to the exploration of both biological systems and computational models.

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Section: Methods Descriptionmentioning

confidence: 99%