Feature Extraction on High Dimensional Data Using Incremental PCA
Byung-Joo Kim · The Journal of the Korean Institute of Information and Communication Engineering · 2004
High dimensional data requires efficient feature extraction techliques. Though PCA(Principal Component Analysis) is a famous feature extraction method it requires huge memory space and computational cost is high. In this paper we use incremental PCA for feature extraction on high dimensional data. Through experiment we show that proposed method is superior to APEX model.