- E. Xing, Nijith Jacob. 2014. 21 : the Indian Buffet Process 2 Latent Feature Models 2.1 Infinite Latent Feature Models. Abstract: In latent class models, such as finite/infinite mixture models, we model a generative process in which the observed data points are realizations from some distribution determined by a single class. In the case of simple finite mixture models, the number of such classes has to be fixed a priori. This limitation is lifted in the case of infinite Dirichlet process mixture models by allowing an infinite number of latent classes. Even though such models can be used to perform clustering with a potentially infinite number of clusters, each point is limited to one cluster. In several real world applications, data points could share multiple classes. For instance, consider the problem of clustering instances of human beings appearing in different contexts across a set of images. We want all such instances to share a cluster for human to different degrees depending on the context in which they appear. To model these problems where data points can share multiple clusters, latent feature models can be used.
