Convex clustering has recently garnered increasing interest due to its attractive theoretical
and computational properties, but its merits become limited in the face of high-dimensional …

Network models provide a powerful and flexible framework for analyzing a wide range of
structured data sources. In many situations of interest, however, multiple networks can be …

The current large amounts of data and advanced technologies have produced new types of
complex data, such as histogram-valued data. The paper focuses on classification problems …

Clustering of time series data exhibits a number of challenges not present in other settings,
notably the problem of registration (alignment) of observed signals. Typical approaches …

Compositional data refer to a vector with parts that are positive and subject to a constant-
sum constraint. Examples of compositional data in the real world include a vector with each …

In this dissertation, we discuss the Majorization-Minimization algorithm and explain how it
improves the current optimization methods by showing three examples. Optimization has …

Clustering analysis is an unsupervised learning technique widely used for information
extraction. Current clustering algorithms often face instabilities due to the non-convex nature …