sJIVE: Supervised Joint and Individual Variation Explained

1 code implementation • 26 Feb 2021 • Elise F. Palzer, Christine Wendt, Russell Bowler, Craig P. Hersh, Sandra E. Safo, Eric F. Lock

We propose a method called supervised joint and individual variation explained (sJIVE) that can simultaneously (1) identify shared (joint) and source-specific (individual) underlying structure and (2) build a linear prediction model for an outcome using these structures.

Latent Space Semi-Supervised Time Series Data Clustering

no code implementations • 1 Jan 2021 • Andrew Hill, Katerina Kechris, Russell Bowler, Farnoush Kashani

Time series data is abundantly available in the real world, but there is a distinct lack of large, labeled datasets available for many types of learning tasks.

Clustering Time Series +1