To use the function "FUP" or "OFUP", please follow the input/output format:
[ W,M,P,objva,r ] = FUP( X,P,m,d1,K,iteration,a) [ W,M,P,objva,r ] = OFUP(X,P,m,d1,K,iteration,lamda,a)
X ： dn, sample matrix, d is the dimension of the sample, n is the number of the number P ： nm, similarity matrix between samples and representitive points, m is the number of the representative points m : number of the representative points d1 : dimension of subspace, d1<d K ： number of nearest neighbors iteration : iteration times a : a==1, isometric sampling initialization for M， else K-means initialization for M W ： dd1 projection matrix objva: 1iteration, the objective function values of all iterations r ： optimized regularization parameter lamda ： hyperparameter
Please make sure that the documents Eu2_distance.m, EProjSimplex_new.m and ClusteringMeasure.m are in the same folder as FUP.m and PFUP.m
Use the codes, please cite Wang J, Wang L, Nie F, et al. Fast Unsupervised Projection for Large-Scale Data[J]. IEEE Transactions on Neural Networks and Learning Systems, 33(8), pp. 3634-3644, 2022.
If you have any questions, please connect wanglinjun@mail.nwpu.edu.cn