1 citations · 1 across the 2 of their papers we have counts for
7 papers
Network Clustering for Latent State and Changepoint Detection
Madeline Navarro, Genevera I. Allen, Michael Weylandt
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 co…
Sparse Partial Least Squares for Coarse Noisy Graph Alignment
Michael Weylandt, George Michailidis, T. Mitchell Roddenberry
Graph signal processing (GSP) provides a powerful framework for analyzing signals arising in a variety of domains. In many applications of GSP, multiple network structures are avai…
Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering
Michael Weylandt, T. Mitchell Roddenberry, Genevera I. Allen
Clustering is a ubiquitous problem in data science and signal processing. In many applications where we observe noisy signals, it is common practice to first denoise the data, perh…
Automatic Registration and Clustering of Time Series
Michael Weylandt, George Michailidis
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 approache…
Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility
Michael Weylandt, Yu Han, Katherine B. Ensor
Financial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial…
Multi-Rank Sparse and Functional PCA: Manifold Optimization and Iterative Deflation Techniques
Michael Weylandt
We consider the problem of estimating multiple principal components using the recently-proposed Sparse and Functional Principal Components Analysis (SFPCA) estimator. We first prop…