... local minimum can be an issue Laplacian Score ( LS ) ( 4 ) Minimum Redundancy Maximum Relevance ( mRMR ) ( 5 ) Correlation - based Feature Selection ( CFS ) ( 6 ) Sparse Subspace Clustering ... preserving the local structure of data • Can ...

... global and local structure of the original input data. It is important to note that this objective function uses a ... subspace clustering. Two cost functions named LLE-SSC and LLE-LRR are proposed [39] with the aid of LLE ...

... local non - negative pursuit method for intrinsic manifold structure preservation . In : Proceedings of AAAI ... subspace clustering : algorithm , theory , and appli- cations . IEEE Trans . Pattern Anal . Mach . Intell . 35 ( 11 ) ...

... clustering via non-negative matrix factorization tailored graph tensor over distributed networks. IEEE J. Select. Areas Commun. (2020) 12. Ren, Z., Sun, Q.: Simultaneous global and local graph structure preserving for multiple kernel ...

24th International Conference, ICONIP 2017, Guangzhou, China, November 14–18, 2017, Proceedings, Part VI Derong Liu, Shengli Xie, Yuanqing Li, Dongbin Zhao, El-Sayed M. El-Alfy. Subspace Clustering via Adaptive Low - Rank Model Mingbo ...

The Fifth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining.

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks.

This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF).

The first part of the book presents applications of spectral methods to problems from a variety of topics including combinatorial optimization, learning and clustering. The second part of the book is motivated by efficiency considerations.

The book Recent Applications in Data Clustering aims to provide an outlook of recent contributions to the vast clustering literature that offers useful insights within the context of modern applications for professionals, academics, and ...