Estimation and Clustering with Infinite Rankings
This work addresses ranking and clustering challenges in scenarios with infinite items, which is incremental as it extends existing finite models to infinite cases.
The paper tackles the problem of ranking with infinitely many items by introducing the infinite generalized Mallows model (IGM) and an Exponential-Blurring-Mean-Shift clustering algorithm for multimodal distributions. The experiments demonstrate that these infinite models are simple, elegant, and practical.
This paper presents a natural extension of stagewise ranking to the the case of infinitely many items. We introduce the infinite generalized Mallows model (IGM), describe its properties and give procedures to estimate it from data. For estimation of multimodal distributions we introduce the Exponential-Blurring-Mean-Shift nonparametric clustering algorithm. The experiments highlight the properties of the new model and demonstrate that infinite models can be simple, elegant and practical.