SPD Data Dictionary Learning Based on Kernel Learning and Riemannian Metric

SPD Data Dictionary Learning Based on Kernel Learning and Riemannian Metric

The use of regional covariance descriptors to generate feature data represented by Symmetric Positive Definite (SPD) matrices from images or videos has become increasingly common in machine learning. However, SPD data itself does not constitute a vector space, and dictionary learning involves a larg...

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Bibliographic Details
Main Authors: Rixin Zhuang, Zhengming Ma, Weijia Feng, Yuanping Lin
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Dictionary learning
symmetric positive definite matrix
reproducing Kernel Hilbert space
Log-Euclidean metric
Online Access:https://ieeexplore.ieee.org/document/9052680/

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