MCA-NMF: Multimodal Concept Acquisition with Non-Negative Matrix Factorization.

ABSTRACT: In this paper we introduce MCA-NMF, a computational model of the acquisition of multimodal concepts by an agent grounded in its environment. More precisely our model finds patterns in multimodal sensor input that characterize associations across modalities (speech utterances, images and motion). We propose this computational model as an answer to the question of how some class of concepts can be learnt. In addition, the model provides a way of defining such a class of plausibly learnable concepts. We detail why the multimodal nature of perception is essential to reduce the ambiguity of learnt concepts as well as to communicate about them through speech. We then present a set of experiments that demonstrate the learning of such concepts from real non-symbolic data consisting of speech sound

SUBMITTER: Mangin O 

PROVIDER: S-EPMC4619362 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature