发明名称 |
ONLINE SPARSE REGULARIZED JOINT ANALYSIS FOR HETEROGENEOUS DATA |
摘要 |
A method and system are provided for online sparse regularized joint analysis for heterogeneous data. The method generates a latent space model modeling a latent space in which correlation information is encoded for a plurality of heterogeneous data points at respective time instants, responsive to respective energy-preserving projections and structure-preserving projections of the data points in the latent space. The method performs online anomaly detection on a current one of the data points responsive to the encoded correlation information for respective ones of the energy-preserving projections and structure-preserving projections for a previous one of the data points without anomaly. The method generates an alarm responsive to a detection of an anomaly for the current one of the data points. The method updates the latent space model for the current one of the data points, by a processor-based online model updater, responsive to a lack of the detection of the anomaly. |
申请公布号 |
US2015095490(A1) |
申请公布日期 |
2015.04.02 |
申请号 |
US201414503562 |
申请日期 |
2014.10.01 |
申请人 |
NEC Laboratories America, Inc. |
发明人 |
Ning Xia;Huang Jiaji;Jiang Guofei |
分类号 |
H04L12/26 |
主分类号 |
H04L12/26 |
代理机构 |
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代理人 |
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主权项 |
1. A method for online sparse regularized joint analysis for heterogeneous data, comprising:
generating a latent space model modeling a latent space in which correlation information is encoded for a plurality of heterogeneous data points at respective ones of a plurality of time instants, responsive to respective energy-preserving projections and structure-preserving projections of the heterogeneous data points in the latent space; performing online anomaly detection on a current one of the plurality of heterogeneous data points responsive to the encoded correlation information for respective ones of the energy-preserving projections and structure-preserving projections for a previous one of the plurality of heterogeneous data points without anomaly; generating an alarm responsive to a detection of an anomaly for the current one of the plurality of heterogeneous data points; and updating the latent space model for the current one of the plurality of heterogeneous data points, by a processor-based online model updater, responsive to a lack of the detection of the anomaly. |
地址 |
Princeton NJ US |