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Found 8 papers, 0 papers with code
A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant Analysis
Much like the classical Fisher linear discriminant analysis (LDA), the recently proposed Wasserstein discriminant analysis (WDA) is a linear dimensionality reduction method that seeks a projection matrix to maximize the dispersion of different data classes and minimize the dispersion of same data classes via a bi-level optimization.
Trace Ratio Optimization with an Application to Multi-view Learning
A trace ratio optimization problem over the Stiefel manifold is investigated from the perspectives of both theory and numerical computations.
Uncorrelated Semi-paired Subspace Learning
However, unpaired data can be more abundant in reality than paired ones and simply ignoring all unpaired data incur tremendous waste in resources.
Orthogonal Multi-view Analysis by Successive Approximations via Eigenvectors
We propose a unified framework for multi-view subspace learning to learn individual orthogonal projections for all views.
Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions
Building on the least squares reformulation of OPLS, we propose a unified multi-view learning framework to learn a classifier over a common latent space shared by all views.
Linear Constrained Rayleigh Quotient Optimization: Theory and Algorithms
We start by equivalently transforming CRQopt into an optimization problem, called LGopt, of minimizing the Lagrangian multiplier of CRQopt, and then an problem, called QEPmin, of finding the smallest eigenvalue of a quadratic eigenvalue problem.
A Self-consistent-field Iteration for Orthogonal Canonical Correlation Analysis
In this paper, we propose an alternating numerical scheme whose core is the sub-maximization problem in the trace-fractional form with an orthogonal constraint.
Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior
Traditional EEG/MEG Source Imaging (ESI) methods usually assume that either source activity at different time points is unrelated, or that similar spatiotemporal patterns exist across an entire study period.
