title: Fast kernel discriminant analysis for classification of liver cancer mass spectra.
abstract: The classification of serum samples based on mass spectrometry (MS) has been increasingly used for monitoring disease progression and for diagnosing early disease. However, the classification task in mass spectrometry data is extremely challenging due to the very huge size of peaks (features) on mass spectra. Linear discriminant analysis (LDA) has been widely used for dimension reduction and feature extraction in many applications. However, the conversional LDA suffers from the singularity problem when dealing with high-dimensional features. Another critical limitation is its linearity property which results in failing in classification problems over nonlinearly clustered data sets. To overcome such problems, we develop a new fast kernel discriminant analysis (FKDA) that is pretty fast in the calculation of optimal discriminant vectors. FKDA is applied to the classification of liver cancer mass spectrometry data that consist of three categories: hepatocellular carcinoma, cirrhosis, and healthy that was originally analyzed by Ressom et al. We demonstrate the superiority and effectiveness of FKDA when compared to other classification techniques.
authors.affiliation: Division of Bioinformatics and Outcomes Research, Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA.
authors.forename: Jung Hun
authors.initials: JH
authors.lastname: Oh
authors.forename: Jean
authors.initials: J
authors.lastname: Gao
mesh_terms: D006528:Carcinoma, Hepatocellular / Q000097:blood* / Q000145:classification / Q000473:pathology; D016002:Discriminant Analysis; D006801:Humans; D008113:Liver Neoplasms / Q000097:blood* / Q000145:classification* / Q000473:pathology; D013058:Mass Spectrometry / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods