Invention Application
- Patent Title: DEEP HIGH-ORDER EXEMPLAR LEARNING FOR HASHING AND FAST INFORMATION RETRIEVAL
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Application No.: US15478840Application Date: 2017-04-04
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Publication No.: US20170293838A1Publication Date: 2017-10-12
- Inventor: Renqiang Min
- Applicant: NEC Laboratories America, Inc.
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N5/04 ; G06F17/30 ; G06N3/04

Abstract:
A system and method are provided for deep high-order exemplar learning of a data set. Feature vectors and class labels are received. Each of the feature vectors represents a respective one of a plurality of high-dimensional data points of the data set. The class labels represent classes for the high-dimensional data points. Each of the feature vectors are processed, using a deep high-order convolutional neural network, to obtain respective low-dimensional embedding vectors within each class. A minimization operation is performed on high-order embedding parameters of the high-dimensional data points to output a set of synthetic exemplars. A binarizing operation is performed on the low-dimensional embedding vectors and the set of synthetic exemplars to output hash codes representing the data set. The hash codes are utilized as a search key to increase the efficiency of a processor-based machine searching the data set.
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