Linear discriminant projection embedding based on patches alignment
Image and Vision Computing
Orthogonal local spline discriminant projection with application to face recognition
Pattern Recognition Letters
Incremental manifold learning by spectral embedding methods
Pattern Recognition Letters
Image classification with manifold learning for out-of-sample data
Signal Processing
Embedding new observations via sparse-coding for non-linear manifold learning
Pattern Recognition
| Hi-index | 0.14 |
We investigate the problem of extrapolating the embedding of a manifold learned from finite samples to novel out-of-sample data. We concentrate on the manifold learning method called Maximum Variance Unfolding (MVU) for which the extrapolation problem is still largely unsolved. Taking the perspective of MVU learning being equivalent to Kernel PCA, our problem reduces to extending a kernel matrix generated from an unknown kernel function to novel points. Leveraging on previous developments, we propose a novel solution which involves approximating the kernel eigenfunction using Gaussian basis functions. We also show how the width of the Gaussian can be tuned to achieve extrapolation. Experimental results which demonstrate the effectiveness of the proposed approach are also included.