고차원 스펙트라 데이터 분석을 위한 Adjusted Direct Orthogonal Signal Correction 기법

Abstract

Modeling and analysis of high-dimensional spectral data provide an opportunity to uncover inherent patterns in various information-rich data. Orthogonal signal correction (OSC), a preprocessing technique has been widely used to remove unwanted variations of spectral data that do not contribute to prediction or classification. In the present study we propose a novel OSC algorithm, called adjusted direct OSC to improve visualization and the ability of classification. Experimental results with real mass spectral data from condom lubricants demonstrate the effectiveness of the proposed approach.