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riemannianpca's Introduction

RiemannianPCA

Dimensionality reduction on manifold of SPD matrices, based on pymanopt implementation

Reference

This project proposes an implementation of

Harandi, M, Salzmann, M., Hartley, R., Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods, preprint arXiv:1605.06182

and

Rodrigues, P. L. C., Bouchard, F., Congedo, M., & Jutten, C. (2017, September). Dimensionality Reduction for BCI classification using Riemannian geometry. In BCI 2017-7th International Brain-Computer Interface Conference.

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riemannianpca's Issues

Correct unsupervised version

When no labels are provided, it is possible to use the unsupervised loss. The implemented version is not correct:

y = np.array([-1] * (n_samples // 2) + [1] * (n_samples // 2))

Intermediate possibility is to use only one label for all the data.

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