Domain Adaption Based on Symmetric Matrices Space Bi-Subspace Learning and Source Linear Discriminant Analysis Regularization

Domain Adaption Based on Symmetric Matrices Space Bi-Subspace Learning and Source Linear Discriminant Analysis Regularization

At present, Symmetric Positive Definite (SPD) matrix data is the most common non-Euclidean data in machine learning. Because SPD data don’t form a linear space, most machine learning algorithms can not be carried out directly on SPD data. The first purpose of this paper is to propose a ne...

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Autores principales: Qian Li, Zhengming Ma, Shuyu Liu, Yanli Pei
Formato: article
Lenguaje:EN
Publicado: IEEE 2021
Materias:
Domain adaptation learning
Riemannian manifold
bi-subspace learning
symmetric positive definite
source linear discriminant analysis regularization
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Acceso en línea:https://doaj.org/article/4dfa2953c2e244aa9f43b97d25fac455
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https://doaj.org/article/4dfa2953c2e244aa9f43b97d25fac455

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