Unsupervised Joint Alignment and Clustering using Bayesian Nonparametrics
This addresses the challenge of aligning and clustering data without restrictive assumptions, which is incremental as it builds on Bayesian nonparametrics for broader applicability.
The paper tackles the problem of unsupervised joint alignment and clustering for complex datasets with multiple modalities by introducing a transformed Bayesian infinite mixture model, achieving large improvements over previous work on synthetic and real data.
Joint alignment of a collection of functions is the process of independently transforming the functions so that they appear more similar to each other. Typically, such unsupervised alignment algorithms fail when presented with complex data sets arising from multiple modalities or make restrictive assumptions about the form of the functions or transformations, limiting their generality. We present a transformed Bayesian infinite mixture model that can simultaneously align and cluster a data set. Our model and associated learning scheme offer two key advantages: the optimal number of clusters is determined in a data-driven fashion through the use of a Dirichlet process prior, and it can accommodate any transformation function parameterized by a continuous parameter vector. As a result, it is applicable to a wide range of data types, and transformation functions. We present positive results on synthetic two-dimensional data, on a set of one-dimensional curves, and on various image data sets, showing large improvements over previous work. We discuss several variations of the model and conclude with directions for future work.