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arXiv Machine Learning By Xinjie Zeng, Qinghua Tao, Johan Suykens

Kernel Singular Value Decomposition with Extension to Multiple Data Sources

Read the original on arXiv Machine Learning →

Kernel Singular Value Decomposition (KSVD) learns singular vectors for an asymmetric kernel matrix derived from two data sources. The paper extends KSVD to multiple data sources, introducing eKSVD, which jointly learns nonlinear feature mappings and captures maximal information across all sources while incorporating pair‑wise couplings. The authors derive a dual optimization framework, a covariance‑based approach, and demonstrate the effectiveness of eKSVD and neural‑network‑based feature mappings through numerical experiments.

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