Publications/ENSIGN

An All–at–Once CP Decomposition Method for Count Tensors

CANDECOMP/PARAFAC (CP) tensor decomposition is a popular method for detecting latent behaviors in real– world data sets. As data sets grow larger and more elaborate, more sophisticated CP decomposition algorithms are required to enable these discoveries. Data sets from many applications can be represented as count tensors. To decompose count

Read More »

Filtered Tensor Construction and Decomposition for Drug Repositioning

Drug repositioning (also called “drug repurposing”) is a drug development strategy that saves time and money by finding new uses for existing drugs. While a variety of computational approaches to drug repositioning exist, recent work has shown that tensor decomposition, an unsupervised learning technique for finding latent structure in multidimensional

Read More »

Knowledge-guided Tensor Decomposition for Baselining and Anomaly Detection

We introduce a flexible knowledge-guided penalty for incorporating known or expected patterns of activity into tensor decomposition. Our modified tensor decomposition both enables efficient identification of semantically-related patterns across data sets and provides a means for identifying anomalous patterns. Specifically, we modify the loss function for a CP tensor decomposition

Read More »

Systems and methods for memory efficient parallel tensor decompositions

In a system for improving performance of tensor-based computations and for minimizing the associated memory usage, computations associated with different non-zero tensor values are performed while exploiting an overlap between the respective index tuples of those non-zero values. While performing computations associated with a selected mode, when an index corresponding

Read More »

Systems and methods for selective expansive recursive tensor analysis

A system for performing tensor decomposition in a selective expansive and/or recursive manner, a tensor is decomposed into a specified number of components, and one or more tensor components are selected for further decomposition. For each selected component, the significant elements thereof are identified, and using the indices of the

Read More »

Large–scale Sparse Tensor Decomposition Using a Damped Gauss–Newton Method

CANDECOMP/PARAFAC (CP) tensor decomposition is a popular unsupervised machine learning method with numerous applications. This process involves modeling a high–dimensional, multi–modal array (a tensor) as the sum of several low–dimensional components. In order to decompose a tensor, one must solve an optimization problem, whose objective is often given by the

Read More »

Fast and Scalable Distributed Tensor Decompositions

Tensor decomposition is a prominent technique for analyzing multi-attribute data and is being increasingly used for data analysis in different application areas. Tensor decomposition methods are computationally intense and often involve irregular memory accesses over large-scale sparse data. Hence it becomes critical to optimize the execution of such data intensive

Read More »