163 citations · 704 across the 74 of their papers we have counts for
26 papers · 1 filter
Sparse-mode Dynamic Mode Decomposition for Disambiguating Local and Global Structures
Sara M. Ichinaga, Steven L. Brunton, Aleksandr Y. Aravkin +1
The dynamic mode decomposition (DMD) is a data-driven approach that extracts the dominant features from spatiotemporal data. In this work, we introduce sparse-mode DMD, a new varia…
Robust Trimmed k-means
Olga Dorabiala, J. Nathan Kutz, Aleksandr Aravkin
Clustering is a fundamental tool in unsupervised learning, used to group objects by distinguishing between similar and dissimilar features of a given data set. One of the most comm…
Time-varying Autoregression with Low Rank Tensors
Kameron Decker Harris, Aleksandr Aravkin, Rajesh Rao +1
We present a windowed technique to learn parsimonious time-varying autoregressive models from multivariate timeseries. This unsupervised method uncovers interpretable spatiotempora…
Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM
German Abrevaya, Irina Rish, Aleksandr Y. Aravkin +7
Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems. In this paper, we focus on brain calcium imaging (CaI) of different organisms…
Sparse Principal Component Analysis via Variable Projection
N. Benjamin Erichson, Peng Zheng, Krithika Manohar +3
Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis, providing improved interpretation of low-rank structures by identifying loc…
Learning Robust Representations for Computer Vision
Peng Zheng, Aleksandr Y. Aravkin, Karthikeyan Natesan Ramamurthy +1
Unsupervised learning techniques in computer vision often require learning latent representations, such as low-dimensional linear and non-linear subspaces. Noise and outliers in th…