Abstract

Today’s densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data, providing temporal characterization of individual behaviors. Is it possible to efficiently couple such rich sensor data with predictive modeling techniques to provide contextual and insightful assessments of individual behavior? Such data is noisy, incomplete, and collected from multiple sources, each with a different temporal resolution and dimensionality. In addition, longitudinal studies tend to examine a number of aspects of human behavior, such as well being, performance, or personality, which poses a challenge for handcrafted features engineering. To address these challenges, we propose supervised tensor embedding (STE), an algorithm for the joint decomposition of input and target variables in high-dimensional multimodal data. Latent features obtained from STE can be fed …

Metadata

publication

2023 IEEE 11th International Conference on Healthcare Informatics (ICHI), 91-100, 2023

year

2023

publication date

2023/6/26

authors

Homa Hosseinmardi, Amir Ghasemian, Kristina Lerman, Shrikanth Narayanan, Emilio Ferrara

link

https://ieeexplore.ieee.org/abstract/document/10337139/

resource_link

https://arxiv.org/pdf/1808.10867

conference

2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)

pages

91-100

publisher

IEEE