Local embedding and dimensionality reduction in detection of skin tumor tissue

• Autorzy: Michalak M. , Świtoński A.
• Języki publikacji: EN

Abstrakty

EN

This article shows the limitation of the usage of dimensionality reduction methods. For this purpose three algorithms were analyzed on the real medical data. This data are multispectral images of human skin labeled as tumor or non-tumor regions. The classification of new data required the special algorithm of new data mapping that is also described in the paper. Unfortunately, the final conclusion is that this kind of local embedding algorithms should not be recommended for this kind of analysis and prediction.

Słowa kluczowe

EN

pattern recognition; multispectral analysis; dimensionality reduction; feature selection

PL

rozpoznawanie wzorców; analiza wielospektralna; redukcja wymiarowości; selekcja cech

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2012
• Tom: Vol. 19
• Strony: 59--65
• Opis fizyczny: Bibliogr. 10 poz., rys., tab.

Twórcy

autor

Michalak M.

• Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

autor

Świtoński A.

Bibliografia

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• [6] MICHALAK M., ŚWITOŃSKI A., STAWARZ M., Selection of the most important components from multispectral images for detection of tumor tissue, Journal of Medical Informatics and Technologies, 2011, Vol. 17, pp. 303-307.
• [7] PEARSON K., On Lines and Planes of Closest Fit to Systems of Points in Space, Philosophical Magazine, 1901, Vol. 2, No. 6, pp. 559–572.
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• [9] ŚWITOŃSKI A., MICHALAK M., JOSIŃSKI H, WOJCIECHOWSKI K., Detection of tumor tissue based on the multispectral imaging, Lecture Notes in Computer Science, 2010, Vol. 6375, pp. 325-333.
• [10] TENENBAUM J.B., De SILVA V., LANGFORD J.C., A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, 2000, Vol. 290, No. 5500, pp. 2319-2323.