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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass Spectra
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Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass Spectra

机译：肝癌质谱分类的快速核判别分析

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摘要

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. [CHECK END OF SENTENCE]. We demonstrate the superiority and effectiveness of FKDA when compared to other classification techniques.
机译：基于质谱（MS）的血清样品分类已越来越多地用于监测疾病的进展和早期疾病的诊断。但是，由于质谱中峰（特征）的尺寸非常大，因此质谱数据中的分类任务极具挑战性。线性判别分析（LDA）已被广泛用于许多应用中的降维和特征提取。但是，转换LDA在处理高维特征时会遇到奇点问题。另一个关键限制是它的线性特性，导致非线性聚类数据集的分类问题失败。为了克服这些问题，我们开发了一种新的快速内核判别分析（FKDA），该算法在计算最佳判别向量时非常快。 FKDA应用于肝癌质谱数据的分类，该数据包括三类：Ressom等最初分析的肝细胞癌，肝硬化和健康。 [检查句子结尾]。与其他分类技术相比，我们证明了FKDA的优越性和有效性。

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