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US 2022/0101068 A1Published application
Outlier detection in a deep neural network using t-way feature combinations
AssigneeInternational Business Machines Corporation
InventorAviad Zlotnick
Life of this patent
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Abstract
Outlier detection using a Deep Neural Network (DNN) includes running a trained DNN model on an received input item. A first feature vector is extracted from the input item and quantized to discrete values. A first number of special t-way feature combinations are computed in the input item and compared against a computed threshold. Based on the comparison, the input item is flagged as an outlier and an alert is generated notifying of the flagged input item.
Claims20
A computer-implemented method for outlier detection, comprising:
- receiving, by one or more processors, an input item;
- running, by the one or more processors, a deep neural network model on the input item;
- extracting, by the one or more processors, a first feature vector from the input item;
- quantizing, by the one or more processors, the extracted first feature vector to discrete values;
- computing, by the one or more processors, a first number of special t-way feature combinations in the input item;
- comparing, by the one or more processors, the computed first number of special t-way feature combinations against a computed threshold;
- based on the comparison, flagging, by the one or more processors, the input item as an outlier;
- and
- generating, by the one or more processors, an alert comprising the flagged input item.
6 more elements
A computer system for outlier detection, comprising:
- one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:
- receiving, by one or more processors, an input item;
- running, by the one or more processors, a deep neural network model on the input item;
- extracting, by the one or more processors, a first feature vector from the input item;
- quantizing, by the one or more processors, the extracted first feature vector to discrete values;
- computing, by the one or more processors, a first number of special t-way feature combinations in the input item;
- comparing, by the one or more processors, the computed first number of special t-way feature combinations against a computed threshold;
- based on the comparison, flagging, by the one or more processors, the input item as an outlier;
- and
- generating, by the one or more processors, an alert comprising the flagged input item.
7 more elements
A computer program product for outlier detection, comprising:
- one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:
- program instructions to receive, by one or more processors, an input item;
- program instructions to run, by the one or more processors, a deep neural network model on the input item;
- program instructions to extract, by the one or more processors, a first feature vector from the input item;
- program instructions to quantize, by the one or more processors, the extracted first feature vector to discrete values;
- program instructions to compute, by the one or more processors, a first number of special t-way feature combinations in the input item;
- program instructions to compare, by the one or more processors, the computed first number of special t-way feature combinations against a computed threshold;
- based on the comparison, program instructions to flag, by the one or more processors, the input item as an outlier;
- and
- program instructions to generate, by the one or more processors, an alert comprising the flagged input item.
7 more elements
Dependencies are inferred from the claim text.