aptMTVL: Nailing Interactions in Multi Task Multi View Multi Label Learning using Adaptive basis Multilinear Factor Analyzers
We investigate a new direction of multi task multi view learning where we have data sets with multiple tasks, multiple views and multiple labels. We call this problem a multi task multi view multi label learning problem or MTVL learning for short. There is a wide application of MTVL leaning where examples include Internet of Things, brain science, and document classification. In designing effective MTVL learning algorithms, we hypothesize that a key component is to "disentangle" interactions among tasks, views, and labels, or the Utask view label interactions. For that purpose we have developed an adaptive basis multilinear analyzers(aptMLFA) that utilizes a loading tensor to modulate interactions among multiple latent factors. With aptMLFA we designed a new MTVL learning algorithm, aptMTVL, and evaluated its performance on 3 real world data sets. The experimental results demonstrated the effectiveness of our proposed method as compared to the state of the art MTVL learning algorithm.
CIKM