Multiple Instance Learning for Behavioral Coding

doi:10.1109/TAFFC.2015.2510625

Multiple Instance Learning for Behavioral Coding

2017-01-01

6

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AI

J

James Gibson *

A

Athanasios Katsamanis

DOI：10.1109/TAFFC.2015.2510625

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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, such as the local versus global nature of behavioral constructs as well as the level of ambiguity in the expression of behaviors through each respective modality. Finally, we assess the success of each modality for behavioral classification and compare schemes for multimodal fusion within the proposed framework.

Keywords:

Behavioral coding

behavioral signal processing

couple therapy

multi-modal signal processing

multiple instance learning

Multiple Instance Learning for Behavioral Coding

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