Abstract

We propose a computational methodology for automatically estimating human behavioral patterns using the multiple instance learning (MIL) paradigm. We describe the incremental diverse density algorithm, a particular formulation of multiple instance learning, and discuss its suitability for behavioral coding. We use a rich multi-modal corpus comprised of chronically distressed married couples having problem-solving discussions as a case study to experimentally evaluate our approach. In the multiple instance learning framework, we treat each discussion as a collection of short-term behavioral expressions which are manifested in the acoustic, lexical, and visual channels. We experimentally demonstrate that this approach successfully learns representations that carry relevant information about the behavioral coding task. Furthermore, we employ this methodology to gain novel insights into human behavioral data …

Date

December 22, 2015

Authors

James Gibson, Athanasios Katsamanis, Francisco Romero, Bo Xiao, Panayiotis Georgiou, Shrikanth Narayanan

Journal

IEEE Transactions on Affective Computing

Volume

8

Issue

1

Pages

81-94

Publisher

IEEE