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- W2538468621 abstract "This dissertation studies basic properties of the combinatorial landscape of Tversky's (1977) contrast model of similarity, and develops and validates a new algorithm (CONOPT) for fitting the contrast model to similarity data. Using methods from combinatorial landscape analysis, I show that the solution space for the feature recovery problem that algorithms for fitting the contrast model must solve is characterized by many local minima, with the number of minima growing as a power law in the size of the problem. Next, I present a new algorithm for fitting the contrast model. The algorithm recovers both t weights and object features from similarity data, under the contrast model. An extensive set of numerical experiments using simulated data validates the algorithm. After validation, I apply the algorithm to several extant data sets taken from experiments on human similarity judgment. The algorithm recovers interpretable feature representations for some of the real data sets: for other data sets, the recovered representations are difficult to interpret. This latter observation reveals one of the greatest shortcomings of feature-based similarity models: Binary feature representations for large numbers of objects can be difficult to make sense of. Finally, general conclusions are drawn from this work." @default.
- W2538468621 created "2016-10-28" @default.
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- W2538468621 date "2004-01-01" @default.
- W2538468621 modified "2023-09-27" @default.
- W2538468621 title "An algorithm for fitting the contrast model" @default.
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