Marginal Discriminant Projection for Coal Mine Safety Data Dimensionality Reduction
نویسندگان
چکیده
Marginal Fisher Analysis (MFA) is a novel dimensionality reduction algorithm. However, the two nearest neighborhood parameters are difficult to select when constructing graphs. In this paper, we propose a nonparametric method called Marginal Discriminant Projection (MDP) which can solves the problem of parameters selection in MFA. Experiment on several benchmark datasets demonstrated the effectiveness of our proposed method, and appreciate performance was achieved when applying on coal mine safety data dimensionality reduction. Streszczenie. W artykule zaproponowano nieparametryczna metodę nazwaną MDOP (marginal discriminant projection) która pomaga rozwiązać problem selekcji danych w algorytmie MFA (marginal Fisher analysis). Metodę zastosowano do redukcji danych w systemach bezpieczeństwa klopalni węglowych. (Metoda Marginal Discriminant Projection w zastosowaniu do redukcji wymiaru danych w systemach bezpieczeństwa kopalni węglowych)
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