The data space was mapped to high dimension feature space with Mercer kernel function, and fuzzy kernel learning vector quantization (FKLVQ) was done on the feature space to obtain the effective and stable clustering weight vectors.
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- 該方法通過(guò)Mercer核,將數據空間映射到高維特征空間,并在此特征空間上進(jìn)行FKLVQ學(xué)習獲取數據空間有效且穩定的聚類(lèi)權矢量,然后在特征空間和輸出空間上僅針對各空間的數據樣本和它們各自的聚類(lèi)權矢量進(jìn)行Sammon非線(xiàn)性核映射。
