Invention Application
US20160012334A1 Hierarchical Sparse Dictionary Learning (HiSDL) for Heterogeneous High-Dimensional Time Series
有权
用于异构高维时间序列的分层稀疏词典学习(HiSDL)
- Patent Title: Hierarchical Sparse Dictionary Learning (HiSDL) for Heterogeneous High-Dimensional Time Series
- Patent Title (中): 用于异构高维时间序列的分层稀疏词典学习(HiSDL)
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Application No.: US14794487Application Date: 2015-07-08
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Publication No.: US20160012334A1Publication Date: 2016-01-14
- Inventor: Xia Ning , Guofei Jiang , Xiao Bian
- Applicant: NEC Laboratories America, Inc.
- Main IPC: G06N5/02
- IPC: G06N5/02 ; G06F17/27 ; G06K9/62

Abstract:
A system, method and computer program product for hierarchical sparse dictionary learning (“HiSDL”) to construct a learned dictionary regularized by an a priori over-complete dictionary, includes providing at least one a priori over-complete dictionary for regularization, performing sparse coding of the at least one a priori over-complete dictionary to provide a sparse coded dictionary, using a processor, updating the sparse coded dictionary with regularization using at least one auxiliary variable to provide a learned dictionary, determining whether the learned dictionary converges to an input data set, and outputting the learned dictionary regularized by the at least one a priori over-complete dictionary when the learned dictionary converges to the input data set. The system and method includes, when the learned dictionary lacks convergence, repeating the steps of performing sparse coding, updating the sparse coded dictionary, and determining whether the learned dictionary converges to the input data set.
Public/Granted literature
- US09870519B2 Hierarchical sparse dictionary learning (HiSDL) for heterogeneous high-dimensional time series Public/Granted day:2018-01-16
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