本研究目的為p大n小資料型態的維度縮減，提出逐步SVM方法，並與未刪減任何變數之研究資料和主成份分析 (PCA)、皮爾森積差相關係數(PCCs)以及基於隨機森林的遞迴特徵消除(RF-RFE) 維度縮減法進行比較，並探討逐步SVM是否能篩選出較能區別樣本類別的特徵集合。研究資料為六筆疾病相關的基因表現以及生物光譜資料。
首先，本研究以監督式學習下使用逐步SVM做特徵選取，從篩選的結果來看，逐步SVM確實能有效從所有變數中萃取出對於樣本的分類上擁有較高重要性之特徵。接著將研究資料分為訓練和測試集，再以半監督式學習下使用逐步SVM、PCA、PCCs和RF-RFE縮減各研究資料之維度，最後配適SVM模型計算預測率，重複以上動作100次取平均當作各維度縮減法的最終預測正確率。觀察計算結果，本研究發現使用逐步SVM所得之預測正確率均優於未處理之原始資料，而與其他方法相比，逐步SVM的穩定度優於PCA和RF-RFE，和PCCs相比則較難看出差異。本研究認為對p大n小資料型態進行維度縮減是必要的，因其能有效消除資料中的雜訊以提升模型整體的預測準確率。

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林宗勳，Support Vector Machine簡介