Tissue Classification Using Efficient Local Fisher Discriminant Analysis
نویسندگان
چکیده
A novel scatter-difference-based local Fisher discriminant analysis(SDLFDA) algorithm for tissue classification is proposed in this paper. SDLFDA explicitly considers the local manifold structure and interclass discrimination in gene expression data space. By using SDLFDA, each gene expression data can be projected into a lower-dimensional discriminative feature space. In addition, SDFLDA reduces the computational cost through QR decomposition. Experimental results demonstrate the effectiveness and efficiency of the proposed SDLFDA algorithm. Streszczenie. W artykule przedstawiono algorytm analizy lokalnym wyróżnikiem Fisher’a opartym na różnicach rozproszenia (ang. SDLFDA), służący do klasyfikacji tkanek. Proponowana metoda pozwala na zmniejszenie wymiarowości przestrzeni wyróżnika, określającego dane GXD, a także redukcję kosztów obliczeniowych dzięki dekompozycji QR. Wyniki badań eksperymentalnych potwierdzają skuteczność i sprawność algorytmu. (Efektywna analiza lokalnego wyróżnika Fisher’a do klasyfikacji tkanek).
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