Discriminant Analysis Methods for Microarray Data Classification

Chapter · January 1970with4 Reads
DOI: 10.1007/978-3-540-89378-3_26 · Source: DBLP
In book: AI 2008: Advances in Artificial Intelligence, pp.268-277

    Abstract

    The studies of DNA Microarray technologies have produced high-dimensional data. In order to alleviate the “curse of dimensionality”
    and better analyze these data, many linear and non-linear dimension reduction methods such as PCA and LLE have been widely
    studied. In this paper, we report our work on microarray data classification with three latest proposed discriminant analysis
    methods: Locality Sensitive Discriminant Analysis (LSDA), Spectral Regression Discriminant Analysis (SRDA), and Supervised
    Neighborhood Preserving Embedding (S-NPE). Results of experiments on four data sets show the excellent effectiveness and efficiency
    of SRDA.