6 papers
Tensor-based second-order causal discovery
Nathan Ouyang, Kexin Wang, Anna Seigal
Causal discovery seeks to uncover the causal dependencies among variables. For this purpose, we propose an algorithm called Tensor-based Second-order Causal Discovery (TSCD). Its i…
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…
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…
A Real Generalized Trisecant Trichotomy
Kristian Ranestad, Anna Seigal, Kexin Wang
The classical trisecant lemma says that a general chord of a non-degenerate space curve is not a trisecant; that is, the chord only meets the curve in two points. The generalized t…
Contrastive independent component analysis
Kexin Wang, Aida Maraj, Anna Seigal
In recent years, there has been growing interest in jointly analyzing a foreground dataset, representing an experimental group, and a background dataset, representing a control gro…
Identifiability of overcomplete independent component analysis
Kexin Wang, Anna Seigal
Independent component analysis (ICA) studies mixtures of independent latent sources. An ICA model is identifiable if the mixing can be recovered uniquely. It is well-known that ICA…