图 书 馆
鲁棒深度自动编码器的异常检测Anomaly Detection with Robust Deep Autoencoders |
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| 课程网址: | http://videolectures.net/kdd2017_zhou_anomaly_detection/ |
| 主讲教师: | Chong Zhou |
| 开课单位: | 伍斯特理工学院 |
| 开课时间: | 2017-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains. However, in many real-world problems, large outliers and pervasive noise are commonplace, and one may not have access to clean training data as required by standard deep denoising autoencoders. Herein, we demonstrate novel extensions to deep autoencoders which not only maintain a deep autoencoders' ability to discover high quality, non-linear features but can also eliminate outliers and noise without access to any clean training data. Our model is inspired by Robust Principal Component Analysis, and we split the input data X into two parts, $X = L_{D} + S$, where $L_{D}$ can be effectively reconstructed by a deep autoencoder and $S$ contains the outliers and noise in the original data X. Since such splitting increases the robustness of standard deep autoencoders, we name our model a "Robust Deep Autoencoder (RDA)". Further, we present generalizations of our results to grouped sparsity norms which allow one to distinguish random anomalies from other types of structured corruptions, such as a collection of features being corrupted across many instances or a collection of instances having more corruptions than their fellows. Such "Group Robust Deep Autoencoders (GRDA)" give rise to novel anomaly detection approaches whose superior performance we demonstrate on a selection of benchmark problems. |
| 关 键 词: | 自动编码; 线性特征; 异常检测 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-06-01：chenxin01 |
| 最后编审: | 2023-06-01：chenxin01 |
| 阅读次数: | 29 |