  Today's densely instrumented world offers tremendous opportunities for
continuous acquisition and analysis of multimodal sensor data providing
temporal characterization of an individual's behaviors. Is it possible to
efficiently couple such rich sensor data with predictive modeling techniques to
provide contextual, and insightful assessments of individual performance and
wellbeing? Prediction of different aspects of human behavior from these noisy,
incomplete, and heterogeneous bio-behavioral temporal data is a challenging
problem, beyond unsupervised discovery of latent structures. We propose a
Supervised Tensor Embedding (STE) algorithm for high dimension multimodal data
with join decomposition of input and target variable. Furthermore, we show that
features selection will help to reduce the contamination in the prediction and
increase the performance. The efficiently of the methods was tested via two
different real world datasets.
