4 accepted papers
This paper investigates metric learning in a Reproducing Kernel Hilbert Space (RKHS) based on a set of random triplet comparisons in the form of *"Do you think item h is more similar to item i or item j?"* indicating similarity and differences between various items. The goal is to learn a metric in
Ideal point based preference learning using pairwise comparisons of type "Do you prefer a or b?" has emerged as a powerful tool for understanding how we make preferences. Existing preference learning approaches assume homogeneity and focus on learning preference on average over the population or req…
We introduce a Bayesian perspective for the structured matrix factorization problem. The proposed framework provides a probabilistic interpretation for existing geometric methods based on determinant minimization. We model input data vectors as linear transformations of latent vectors drawn from a d…
Polytopic Matrix Factorization (PMF) is introduced as a flexible data decomposition tool with potential applications in unsupervised learning. PMF assumes a generative model where observations are lossless linear mixtures of some samples drawn from a particular polytope. Assuming that these samples…