Research on Improved Locally Linear Embedding Algorithm

Deng, Tuo; Deng, Yanni; Shi, Yuan; Zhou, Xin

doi:10.1007/978-3-662-45049-9_15

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…There exist many clustering methods that utilize graph data, such as Ratio Cut based methods [ 16 , 17 ] and Normalized Cut based methods [ 18 , 19 ] and Min and Max Cut based methods [ 20 , 21 ]. In essence, these clustering methods first embed graph nodes in low-dimensional space using linear embedding method PCA and nonlinear method IsoMAP [ 22 – 24 ], Local linear Embedding (LLE) [ 25 – 27 ], Local Tangent Space Alignment [ 26 , 28 , 29 ] etc, where feature vector information is utilized to obtain the final clustering results. However, It’s one-sided to just take into account of one single information.…”

Section: Introductionmentioning

confidence: 99%

Elastic K-means using posterior probability

Zheng

Jiang

Li³

et al. 2017

PLoS ONE

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The widely used K-means clustering is a hard clustering algorithm. Here we propose a Elastic K-means clustering model (EKM) using posterior probability with soft capability where each data point can belong to multiple clusters fractionally and show the benefit of proposed Elastic K-means. Furthermore, in many applications, besides vector attributes information, pairwise relations (graph information) are also available. Thus we integrate EKM with Normalized Cut graph clustering into a single clustering formulation. Finally, we provide several useful matrix inequalities which are useful for matrix formulations of learning models. Based on these results, we prove the correctness and the convergence of EKM algorithms. Experimental results on six benchmark datasets demonstrate the effectiveness of proposed EKM and its integrated model.

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