Comparison studies on active cross-situational object-word learning using Non-Negative Matrix Factorization and Latent Dirichlet Allocation - Archive ouverte HAL Access content directly
Journal Articles IEEE Transactions on Cognitive and Developmental Systems Year : 2018

Comparison studies on active cross-situational object-word learning using Non-Negative Matrix Factorization and Latent Dirichlet Allocation

Abstract

Future intelligent robots are expected to be able to adapt continuously to their environment. For this purpose, recognizing new objects and learning new words through interactive learning with humans is fundamental. Such setup results in ambiguous teaching data which humans have been shown to address using cross-situational learning, i.e. by analyzing common factors between multiple learning situations. Moreover, they have been shown to be more efficient when actively choosing the learning samples, e.g. which object they want to learn. Implementing such abilities on robots can be performed by latent-topic learning models such as Non-Negative Matrix Factorization or Latent Dirichlet Allocation. These cross-situational learning methods tackle referential and linguistic ambiguities, and can be associated with active learning strategies. We propose two such methods: the Maximum Reconstruction Error based Selection (MRES) and Confidence Base Exploration (CBE). We present extensive experiments using these two learning algorithms through a systematic analysis on the effects of these active learning strategies in contrast with random choice. In addition, we study the factors underlying the active learning by focusing on the use of sample repetition, one of the learning behaviors that have been shown to be important for humans.
Fichier principal
Vignette du fichier
comparison-studies-active_final.pdf (4.59 Mo) Télécharger le fichier
Vignette du fichier
Yuxin.jpg (51.12 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Format Figure, Image
Loading...

Dates and versions

hal-01561168 , version 1 (12-07-2017)

Identifiers

Cite

Yuxin Chen, Jean-Baptiste Bordes, David Filliat. Comparison studies on active cross-situational object-word learning using Non-Negative Matrix Factorization and Latent Dirichlet Allocation. IEEE Transactions on Cognitive and Developmental Systems, 2018, ⟨10.1109/TCDS.2017.2725304⟩. ⟨hal-01561168⟩
348 View
190 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More