Focused on the issue that the category relationship between samples is not considered in the unsupervised Locally Invariant Robust Principal Component Analysis （LIRPCA） algorithm， a feature extraction model based on Neighbor Supervised LIRPCA （NSLIRPCA） was proposed. The category information between samples was considered by the proposed model， and a relationship matrix was constructed based on this information. The formulas of the model were solved and the convergences of the formulas were proved. At the same time， the proposed model was applied to various occlusion datasets. Experimental results show that compared with Principal Component Analysis （PCA）， PCA based on L1-norm （PCA-L1）， Non-negative Matrix Factorization （NMF）， Locality Preserving Projection （LPP） and LIRPCA algorithms on ORL， Yale， COIL-Processed and PolyU datasets， the proposed model has the recognition rate improved by 8.80%， 7.76%， 20.37%， 4.72% and 4.61% at most respectively on the original image datasets， and the recognition rate improved by 30.79%， 30.73%， 36.02%， 19.65% and 17.31% at most respectively on the occluded image datasets. It can be seen that with the proposed model， the recognition performance of the algorithm is improved， and the complexity of the model is reduced， verifying that the model is obviously better than the comparison algorithms.
