Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer

Slideshow

Wenxuan Zhong

UGA Athletic Association Professor
wenxuan@uga.edu
University of Illinois at Urbana-Champaign

Recently, a low cost yet highly sensitive colorimetric sensor array (CSA) for the detection and identification of volatile chemical toxicants has been developed. Classification analysis holds the key to the success of the array in discriminating multiple toxicants. The data output by the CSA are in the form of matrices, which render many traditional classification methods inapplicable. In this talk, I will introduce a matrix discriminant analysis method which can be viewed as a generalization of the conventional LDA method to the data in matrices form. By incorporating the intrinsic matrix structure of the data, the proposed method can greatly improve the sensitivity, and more importantly, the specificity of toxicants classification using the CSA. To further reduce the misclassification rate, I will introduce the $l_1$ penalty into the proposed matrix discriminant analysis to shrink the effect of the discriminant-irrelevant predictors. Two algorithms are developed to estimate parameters in the matrix discriminant analyses. Numerical studies suggest that the proposed matrix discriminant methods outperform the traditional LDA method. The asymptotic consistency is also established to provide the insight of the excellence of the empirical performance.

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.