Neural feature abstraction from judgements of similarity
Neural Computation
ACM Computing Surveys (CSUR)
Determining the dimensionality of multidimensional scaling representations for cognitive modeling
Journal of Mathematical Psychology
Fisher information and stochastic complexity
IEEE Transactions on Information Theory
Accelerated sampling for the Indian Buffet Process
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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One of the central problems in cognitive science is determining the mental representations that underlie human inferences. Solutions to this problem often rely on the analysis of subjective similarity judgments, on the assumption that recognizing likenesses between people, objects, and events is crucial to everyday inference. One such solution is provided by the additive clustering model, which is widely used to infer the features of a set of stimuli from their similarities, on the assumption that similarity is a weighted linear function of common features. Existing approaches for implementing additive clustering often lack a complete framework for statistical inference, particularly with respect to choosing the number of features. To address these problems, this article develops a fully Bayesian formulation of the additive clustering model, using methods from nonparametric Bayesian statistics to allow the number of features to vary. We use this to explore several approaches to parameter estimation, showing that the nonparametric Bayesian approach provides a straightforward way to obtain estimates of both the number of features and their importance.