Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and "linear inseparability" of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher's Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of "potential" inverse covariance matrix estimates using a Wishart distribution estimated from the training data. Then, FWDA samples a group of inverse covariance matrices from the Wishart distribution, predicts using LDA classifiers based on the sampled inverse covariance matrices, and "weighted-averages" the prediction results via Bayesian Voting scheme. The weights for voting are optimally updated to adapt each new input data, so as to enable the nonlinear classification.

Meeting Name

Complex Adaptive Systems Conference with Theme: Cyber Physical Systems and Deep Learning, CAS 2018 (2018: Nov. 5-7, Chicago, IL)


Engineering Management and Systems Engineering

Second Department

Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Early Detection; Electronic Health Records; Fisher's Wishart Discriminant Analysis; Linear Discriminant Analysis

International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





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Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Publication Date

01 Nov 2018