@inproceedings{910f3c6f097448ae95c5b38a5243675f,

title = "Multi-way clustering using super-symmetric non-negative tensor factorization",

abstract = "We consider the problem of clustering data into k ≥ 2 clusters given complex relations - going beyond pairwise - between the data points. The complex n-wise relations are modeled by an n-way array where each entry corresponds to an affinity measure over an n-tuple of data points. We show that a probabilistic assignment of data points to clusters is equivalent, under mild conditional independence assumptions, to a super-symmetric non-negative factorization of the closest hyper-stochastic version of the input n-way affinity array. We derive an algorithm for finding a local minimum solution to the factorization problem whose computational complexity is proportional to the number of n-tuple samples drawn from the data. We apply the algorithm to a number of visual interpretation problems including 3D multi-body segmentation and illumination-based clustering of human faces.",

author = "Amnon Shashua and Ron Zass and Tamir Hazan",

year = "2006",

doi = "10.1007/11744085_46",

language = "American English",

isbn = "3540338381",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "595--608",

booktitle = "Computer Vision - ECCV 2006, 9th European Conference on Computer Vision, Proceedings",

note = "9th European Conference on Computer Vision, ECCV 2006 ; Conference date: 07-05-2006 Through 13-05-2006",

}