Abstract: An incoherent dictionary learning and sparse representation algorithm is proposed for histopathological image classification in this paper.Class-specific sub-dictionaries are firstly learned from each class training samples by exploring online dictionary learning.Furthermore,a novel incoherent dictionary learning model is designed by introducing tight frame.This model can optimize the difference between Gram matrix and reference Gram matrix by alternating projection on coherence,rank and tight frame of dictionary.The high-quality discriminative incoherent dictionary is obtained.To obtain the preferable sparse representation performance,subspace rotation method is utilized to optimize the sparse representation performance of incoherent dictionary.Experimental results on BreaKHis dataset show that the learned incoherent dictionary can trade-off the discriminative ability and sparse representation.The proposed method achieves 86.0%of classification accuracy on benign and malignant tumors image,and 92.5%of classification accuracy on adenosis and fibroadenoma image.