三种判别分析方法在元音库上的分类

Classification About Vowel Database Using Discriminant Analysis Methods

分别用降秩线性判别分析(RRLDA)、降秩二次判别分析(RRQDA)和主成分分析+线性判别分析(PCA+LDA)三种模型对数据进行了分析,并在元音测试数据集上进行了测试。分别画出了这三种模型的误分类率曲线,画出了RRLDA和PCA+LDA分别降至二维后的最优分类面。从实验结果中可以发现,RRLDA模型的实验结果优于PCA+LDA模型,而RRQDA的误分类率相当的高,这是因为PCA在降维过程中仅仅要求数据分散,而忽略了数据的类内和类间的信息。同时,曲线提示RRLDA在子空间的维数取2时具有最好的泛化能力,PCA+LDA在子空间的维数取4时具有最好的泛化能力,RRQDA在第10维才有最好的检验误差率。

The paper analyzes vowel data using reduced-rank linear discriminant analysis （RRLDA）, reduced-rank quadratic discriminant analysis （RRQDA） and principal component analysis plus linear discriminant analysis （PCA＋LDA）. Then it drew some curves of false classification about the three model. A curved surface of the best classification has drawn for RRLDA and PCA＋LDA after reduced rank to two dimensions. From the result, it can be conclude that RRLDA is good than PCA＋LDA. The false classification of RRQDA is considerably big, because PCA ignores the information of classification about data and only disperses data during reducing rank. Simultaneously these curves prompts RRLDA owning the best generalizing ability when its dimension is 2 in subspace, and PCA＋LDA owning the best generalizing ability when its dimension is 4 in subspace, and RRQDA owning the best verify error rate in tenth dimension.