Spatiotemporal method for anomaly detection in dictionary learning and sparse signal recognition

• 1. 一种基于时空拉普拉斯特征映射学习的异常行为检测方法 [P] . 中国专利： CN105787472B . 2019.02.15
• 2. 一种基于时空拉普拉斯特征映射学习的异常行为检测方法 [P] . 中国专利： CN105787472A . 2016-07-20
• 3. Spatiotemporal Method for Anomaly Detection in Dictionary Learning and Sparse Signal Recognition [P] . 美国专利： US2017286811A1 . 2017-10-05

  机译： 词典学习和稀疏信号识别中的时空异常检测方法

• 4. IDENTIFICATION DICTIONARY LEARNING SYSTEM, RECOGNITION DICTIONARY LEARNING METHOD AND RECOGNITION DICTIONARY LEARNING PROGRAM [P] . 日本专利： JP2016062249A . 2016-04-25

  机译： 识别词典学习系统，识别词典学习方法和识别词典学习程序

• 5. Learning apparatus, pattern recognition apparatus, coding apparatus, sorting apparatus, and learning method of pattern recognition dictionary of pattern recognition dictionary [P] . 日本专利： JP5992206B2 . 2016-09-14

  机译： 模式识别字典的模式识别字典的学习设备，模式识别设备，编码设备，分类设备和学习方法

• 1. Pre-detection and dual-dictionary sparse representation based face recognition algorithm in non-sufficient training samples [J] . ZHAO Jian, ZHANG Chao, ZHANG Shunli, 系统工程与电子技术（英文版） . 2018,第001期
• 2. Rare Bird Sparse Recognition via Part-Based Gist Feature Fusion and Regularized Intraclass Dictionary Learning [J] . Jixin Liu, Ning Sun, Xiaofei Li, 计算机、材料和连续体(英文) . 2018,第006期
• 3. A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning [J] . Chuan Sun, Hongpeng Yin, Yanxia Li, 自动化学报：英文版 . 2021,第006期
• 4. A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning [J] . Chuan Sun, Hongpeng Yin, Yanxia Li, 自动化学报（英文版） . 2021,第006期
• 5. Comparison and Adaptation of Two Strategies for Anomaly Detection in Load Profiles Based on Methods from the Fields of Machine Learning and Statistics [J] . Patrick Krawiec, Mark Junge, Jens Hesselbach 能源效率（英文） . 2021,第002期
• 6. Sparse representation and overcomplete dictionary learning for anomaly detection in electrocardiograms [J] . Neural computing & applications . 2020,第5期
• 7. Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal [J] . Udit Satija, Barathram Ramkumar, M. Sabarimalai Manikandan Healthcare Technology Letters . 2017,第1期
• 8. Dictionary Learning with Incoherence and Sparsity Constraints for Sparse Representation of Nonnegative Signals [J] . Zunyi TANG, Shuxue DING IEICE transactions on information and systems . 2013,第5期
• 9. A sparse dictionary learning method for hyperspectral anomaly detection with capped norm [C] . Dandan Ma, Yuan Yuan, Qi Wang International Geoscience and Remote Sensing Symposium . 2017
• 10. Detection and classification of non-stationary signals using sparse representations in adaptive dictionaries. [D] . Moody, Daniela I. 2012
• 11. Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition [O] . Xin Tang, Guo-can Feng, Xiao-xin Li, -1
• 12. Dictionary selection for compressed sensing of EEG signals using sparse binary matrix and spatiotemporal sparse Bayesian learning [O] . Manika Rani Dey, Arsam Shiraz, Saeed Sharif, 2020
• 13. Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization [R] . Duarte-Carvajalino, J. M., Sapiro, G. 2008

• 1. 汉、英学习词典与理解型词典中动作义位释义的对比分析 [C] . 于屏方 . 第二届对外汉语学习词典学国际研讨会 . 2006
• 2. 基于时空特征学习的异常检测与行为识别 [A] . 李谣顺 . 2019