Batch-incremental principal component analysis with exact mean update
Guifang Duan, Yen‐Wei Chen · 2011
Incremental principal component analysis (IPCA) has been of great interest in computer vision and machine learning. In this paper, we introduce a new incremental learning procedure for principal component analysis (PCA). The proposed method can keep an accurate track of the mean of the data, and can deal with a set of new observed data in batch each time in subspace updating. Furthermore, a weighting function is proposed for contribution balance of the current data and the new observed data to the new subspace. The performance of our method is illustrated in the experiments on face modeling and face recognition.