kernel canonical correlation analysis

基本解釋

Kernel Canonical Correlation Analysis(KCCA) is a recently addressed supervised machine learning methods, which is a powerful approach of extracting nonlinear features.
針對(duì)該問題，采用核典型相關(guān)分析方法進(jìn)行原始特征的二次提取，得到簡(jiǎn)約而重要的二次特征。
By introducing the kernel trick to the canonical correlation analysis(CCA), a feature fusion method based on kernel CCA(KCCA) is established and is then used to capture the associated feat.
該方法首先采集側(cè)面視角人臉圖像，然后將核方法引入到典型相關(guān)分析（CCA）中，提出基于核CCA的特征融合方法，并應(yīng)用其提取人耳人臉的關(guān)聯(lián)特征進(jìn)行個(gè)體的分類識(shí)別。