Tensor Embedding: A Supervised Framework for Human Behavioral Data Mining and Prediction
This work addresses the challenge of behavioral prediction for individuals using sensor data, but it appears incremental as it builds on existing tensor methods with added supervision and feature selection.
The authors tackled the problem of predicting human behavior from noisy, multimodal sensor data by proposing a Supervised Tensor Embedding (STE) algorithm with joint decomposition and feature selection, achieving improved performance as tested on two real-world datasets.
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.