Tensor sufficient dimension reduction

Wenxuan Zhong, Xin Xing, Kenneth Suslick

Research output: Contribution to journalArticlepeer-review


Tensor is a multiway array. With the rapid development of science and technology in the past decades, large amount of tensor observations are routinely collected, processed, and stored in many scientific researches and commercial activities nowadays. The colorimetric sensor array (CSA) data is such an example. Driven by the need to address data analysis challenges that arise in CSA data, we propose a tensor dimension reduction model, a model assuming the nonlinear dependence between a response and a projection of all the tensor predictors. The tensor dimension reduction models are estimated in a sequential iterative fashion. The proposed method is applied to a CSA data collected for 150 pathogenic bacteria coming from 10 bacterial species and 14 bacteria from one control species. Empirical performance demonstrates that our proposed method can greatly improve the sensitivity and specificity of the CSA technique.

Original languageEnglish (US)
Pages (from-to)178-184
Number of pages7
JournalWiley Interdisciplinary Reviews: Computational Statistics
Issue number3
StatePublished - May 1 2015


  • Dimension reduction
  • Iterative estimation
  • Sliced inverse regression
  • Tensor analysis

ASJC Scopus subject areas

  • Statistics and Probability


Dive into the research topics of 'Tensor sufficient dimension reduction'. Together they form a unique fingerprint.

Cite this