Nov 1, 2022 · We propose a trace ratio formulation for multi-view subspace learning to learn individual orthogonal projections for all views.

Oct 4, 2020 · An efficient numerical method based on successive approximations via eigenvectors is presented to solve the associated optimization problem. The ...

Nov 1, 2022 · We propose a trace ratio formulation for multi-view subspace learning to learn individual orthogonal projections for all views.

To solve the challenging problem, we propose an efficient optimization method called orthogonal successive approximation via eigenvectors (OSAVE).

An efficient numerical method based on successive approximations via eigenvectors is presented to solve the associated optimization problem. The method is built ...

The method is built upon an iterative Krylov subspace method which can easily scale up for high-dimensional datasets. Extensive experiments are conducted on ...

Semantic Scholar extracted view of "Orthogonal Multi-view Analysis by Successive Approximations via Eigenvectors" by L. xilinx Wang et al.

About: This article is published in Neurocomputing.The article was published on 2022-11-01 and is currently open access. It has received 2 citations till now.

Orthogonal multi-view analysis by successive approximations via eigenvectors. https://doi.org/10.1016/j.neucom.2022.09.018 ·. Journal: Neurocomputing, 2022, p ...

We propose a unified framework for multi-view subspace learning to learn individual orthogonal projections for all views. The framework integrates the ...