Unsupervised Learning, Recognition, and Generation of Time-series Patterns Based on Self-Organizing Segmentation

Abstract

This study is intended to realize a motion recognition and generation mechanism based on observation. This mechanism, which is based on imitative learning, enables unsupervised incremental learning, recognition, and generation of time-series patterns that are observed directly from motion images. The mechanism segments these patterns into primitives in a self-organized manner using mixture-of-experts (MoE) with a non-monotonous neural network (NMNN). These patterns are expressed as permutations of primitives that are output by the MoE. Applying enhanced dynamic time warping (DTW) method recognizes these permutations of primitives. In addition, we introduce a semi-supervised learning method by applying this mechanism. We confirmed the effectiveness of this mechanism through two experiments using gestures