Metric Learning for Estimating Psychological Similarities
Abstract
An important problem in cognitive psychology is to quantify the perceived similarities between stimuli. Previous work attempted to address this problem with multidimensional scaling (MDS) and its variants. However, there are several shortcomings of the MDS approaches. We propose Yada, a novel general metric-learning procedure based on two-alternative forced-choice behavioral experiments. Our method learns forward and backward nonlinear mappings between an objective space in which the stimuli are defined by the standard feature vector representation and a subjective space in which the distance between a pair of stimuli corresponds to their perceived similarity. We conduct experiments on both synthetic and real human behavioral datasets to assess the effectiveness of Yada. The results show that Yada outperforms several standard embedding and metric-learning algorithms, both in terms of likelihood and recovery error.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 01, 2012
- Source ID
- 10.1145/2168752.2168769
Entities
People
- Jun-ming Xu
- Timothy T. Rogers
- Xiaojin Zhu
Organizations
- Air Force Office of Scientific Research
- Division of Information and Intelligent Systems
- University of Wisconsin–Madison