International Business Machines Corporation

University of Minnesota

Arindam Banerjee

Ramesh Natarajan

Edison Lao Ting

Richard D Lawrence

Vikas Sindhwani

Prem Melville

Saha Ankan

Shiva P Kasiviswanathan

Hanhuai Shan

A method, system and computer program product for inferring topic evolution and emergence in a set of documents. In one embodiment, the method comprises forming a group of matrices using text in the documents, and analyzing these matrices to identify a first group of topics as evolving topics and a second group of topics as emerging topics. The matrices includes a first matrix X identifying a multitude of words in each of the documents, a second matrix W identifying a multitude of topics in each of the documents, and a third matrix H identifying a multitude of words for each of the multitude of topics. These matrices are analyzed to identify the evolving and emerging topics. In an embodiment, the documents form a streaming dataset, and two forms of temporal regularizers are used to help identify the evolving topics and the emerging topics in the streaming dataset.

Inferring emerging and evolving topics in streaming text

A system, method and computer program product provides for multiple imputation of missing data elements in retail data sets used for modeling and decision-support applications based on the multi-dimensional, tensor structure of the data sets, and a fast, scalable scheme is implemented that is suitable for large data sets. The method generates multiple imputations comprising a set of complete data sets each containing one of a plurality of imputed realizations for the missing data values in the original data set, so that the variability in the magnitudes of these missing data values can be captured for subsequent statistical analysis. The method is based on the multi-dimensional structure of the retail data sets incorporating tensor factorization, that in a preferred embodiment can be implemented using fast, scalable imputation methods suitable for large data sets, to obtain multiple complete data sets in which the original missing values are replaced by various imputed values.

Multiple imputation of missing data in multi-dimensional retail sales data sets via tensor factorization

Growing community of inventors

Roseville, MN, United States of America

Arindam Banerjee