16 citations · 34 across the 7 of their papers we have counts for
6 papers · 1 filter
TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records
Ardavan Afshar, Ioakeim Perros, Haesun Park +5
Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of p…
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping
Ioakeim Perros, Evangelos E. Papalexakis, Haesun Park +5
This paper presents a new method, which we call SUSTain, that extends real-valued matrix and tensor factorizations to data where values are integers. Such data are common when the…
COPA: Constrained PARAFAC2 for Sparse & Large Datasets
Ardavan Afshar, Ioakeim Perros, Evangelos E. Papalexakis +3
PARAFAC2 has demonstrated success in modeling irregular tensors, where the tensor dimensions vary across one of the modes. An example scenario is modeling treatments across a set o…
SPARTan: Scalable PARAFAC2 for Large & Sparse Data
Ioakeim Perros, Evangelos E. Papalexakis, Fei Wang +4
In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling m…
Sparse Hierarchical Tucker Factorization and its Application to Healthcare
Ioakeim Perros, Robert Chen, Richard Vuduc +1
We propose a new tensor factorization method, called the Sparse Hierarchical-Tucker (Sparse H-Tucker), for sparse and high-order data tensors. Sparse H-Tucker is inspired by its na…
Guaranteed Scalable Learning of Latent Tree Models
Furong Huang, Niranjan U. N., Ioakeim Perros +3
We present an integrated approach for structure and parameter estimation in latent tree graphical models. Our overall approach follows a "divide-and-conquer" strategy that learns m…