[1] von LUXBURG U. A tutorial on spectral clustering[J]. Statistics and Computing, 2007,17(4):395-416. [2] REBAGLIATI N, VERRI A. Spectral clustering with more than k eigenvectors[J]. Neurocomputing, 2011,74(9):1391-1401. [3] LU H, FU Z, SHU X. Non-negative and sparse spectral clustering[J]. Pattern Recognition, 2014,47(1):418-426. [4] CHEN W, FENG G. Spectral clustering:a semi-supervised approach[J]. Neurocomputing, 2012,77(1):229-242. [5] WU S, FENG X, ZHOU W. Spectral clustering of high-dimensional data exploiting sparse representation vectors[J]. Neurocomputing, 2014,135(7):229-239. [6] NIE F, ZENG Z, TSANG I W,et al. Spectral embedded clustering:a framework for in-sample and out-of-sample spectral clustering[J]. IEEE Transactions on Neural Networks, 2011,22(11):1796-1808. [7] JIAO L C, SHANG F, WANG F,et al. Fast semi-supervised clustering with enhanced spectral embedding[J]. Pattern Recognition, 2012,45(12):4358-4369. [8] FIEDER M. Algebraic connectivity of graphs[J]. Czechoslovak Mathematical Journal, 1973,23(2):298-305. [9] CHEN W, FENG G. Spectral clustering with discriminant cuts[J]. Knowledge-Based Systems, 2012,28:27-37. [10] ZHONG S, CHEN D, XU Q,et al. Optimizing the Gaussian kernel function with the formulated kernel target alignment criterion for two-class pattern classification[J]. Pattern Recognition, 2013,46(7):2045-2054. [11] FILIPPONE M, CAMASTRA F, MASULLI F,et al. A survey of kernel and spectral methods for clustering[J]. Pattern Recognition, 2008,41(1):176-190. [12] ZHANG X, LI J, YU H. Local density adaptive similarity measurement for spectral clustering[J]. Pattern Recognition Letters, 2011,32(2):352-358. |