本文選題：基于 + 成分　； 參考：《財(cái)會(huì)通訊》2013年02期

【摘要】：正一、引言主成分分析也稱主分量分析,旨在利用降維的思想,通過對(duì)原始指標(biāo)的相關(guān)矩陣內(nèi)部結(jié)構(gòu)關(guān)系的研究,找出影響某一狀況的幾個(gè)綜合指標(biāo)即主成分,使綜合指標(biāo)為原始指標(biāo)的線性組合,綜合指標(biāo)不僅保留了原始指標(biāo)的信息,彼此又不相關(guān),使在研究復(fù)雜的問題時(shí)能夠抓住主要矛盾,用較少的變量去解釋原始指標(biāo)的大部分變異。主成分分析法作為一種客觀賦權(quán)法,在計(jì)算綜合評(píng)價(jià)值時(shí),
[Abstract]:First, the introduction of principal component analysis, also called principal component analysis (PCA), aims to find out several comprehensive indexes that affect a certain situation, namely, principal components, through the study of the internal structure relationship of the correlation matrix of the original index by using the idea of dimensionality reduction. Making the composite index a linear combination of the original index, which not only retains the information of the original index, but also is not related to each other, so that the principal contradiction can be grasped in the study of complex problems. Most variations in the original indicator are explained with fewer variables. As an objective weighting method, Principal component Analysis (PCA) is used to calculate the comprehensive evaluation value.
【作者單位】： 桂林電子科技大學(xué);
【基金】：廣西研究生教育創(chuàng)新計(jì)劃資助項(xiàng)目(編號(hào):2011105950202M41)的階段性研究成果
【分類號(hào)】：F224;F426.72;F406.72
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本文編號(hào)：1825123