1 citations · 1 across the 4 of their papers we have counts for
5 papers
Group Invariant Spectral Embedding
Yeari Vigder, Paulina Hoyos, David Thong +3
Spectral embedding methods are widely used for dimensionality reduction and clustering of high-dimensional datasets with intrinsic low-dimensional structures. Although many dataset…
Multi-context principal component analysis
Kexin Wang, Salil Bhate, João M. Pereira +3
Principal component analysis (PCA) is a tool to capture factors that explain variation in data. Across domains, data are now collected across multiple contexts (for example, indivi…
SO(3)-invariant PCA with application to molecular data
Michael Fraiman, Paulina Hoyos, Tamir Bendory +4
Principal component analysis (PCA) is a fundamental technique for dimensionality reduction and denoising; however, its application to three-dimensional data with arbitrary orientat…
Multi-subspace power method for decomposing partially symmetric tensors
Kexin Wang, João M. Pereira, Joe Kileel +1
We present an algorithm for low rank decomposition of tensors of any symmetry type, from fully asymmetric to fully symmetric. It recovers the decomposition one summand at a time vi…
Diffusion Maps for Group-Invariant Manifolds
Paulina Hoyos, Joe Kileel
In this article, we consider the manifold learning problem when the data set is invariant under the action of a compact Lie group . Our approach consists in augmenting the data-…