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    Adaptive Method for Outlier Detection and Spectral Library Augmentation
    1.
    发明申请
    Adaptive Method for Outlier Detection and Spectral Library Augmentation 审中-公开
    异常检测和光谱库扩增的自适应方法

    公开(公告)号：US20090012723A1

    公开(公告)日：2009-01-08

    申请号：US12196921

    申请日：2008-08-22

    申请人： Patrick J. TREADO ,  Robert Schweitzer ,  Jason Neiss

    发明人： Patrick J. TREADO ,  Robert Schweitzer ,  Jason Neiss

    IPC分类号： G01N31/00 ,  G06F19/00

    CPC分类号： G16C20/20 ,  G16C20/90

    摘要： A method for analyzing data from an unknown substance, whereby target data representative of an unknown substance is received and compared to reference data associated with one or more known substances. Such comparison determines one or more candidate substances. After determining candidate substances, it is determined if the target data is unique to a candidate substance. If the target data is unique to one of the candidate substances, then this determination is confirmed with fusion. If the target data is not unique, then the target data may be subjected to fusion and unmixing with fusion. If analysis of the target data determines that an outlier is present, then this target data is added to a pool of unassigned data. The addition of this new data to the pool of unassigned data may result in clustering of enough of the previously unassigned data to form a new candidate class. If analysis of the target data does not detect an outlier, but cannot be matched to an existing candidate class, the target data in this case can also be added to the pool of unassigned data. If no outlier is detected, and the Matching Existing Class step is successful, then the target data is added to the matched class. If this candidate class is confirmed, then it can be added to the list of existing classes.

    摘要翻译： 用于分析来自未知物质的数据的方法，由此接收表示未知物质的目标数据并与与一种或多种已知物质相关联的参考数据进行比较。 这种比较确定一种或多种候选物质。 在确定候选物质之后，确定目标数据是否对于候选物质是唯一的。 如果目标数据对于候选物质之一是唯一的，则通过融合确认该确定。 如果目标数据不是唯一的，则可以对目标数据进行融合和解混合。 如果目标数据的分析确定存在异常值，则该目标数据被添加到未分配数据的池中。 将此新数据添加到未分配数据池可能会导致足够的以前未分配数据的聚类，以形成新的候选类。 如果目标数据的分析没有检测到异常值，但是不能与现有候选类别匹配，则在这种情况下的目标数据也可以被添加到未分配数据的池中。 如果没有检测到异常值，并且匹配现有类步骤成功，则将目标数据添加到匹配的类中。 如果这个候选类被确认，那么它可以被添加到现有类的列表中。