Efficient subspace clustering based on self-representation and grouping effect

Abstract

Traditional subspace clustering methods [such as sparse subspace clustering (SSC), least squares representation (LSR) and smooth representation clustering] either considered the grouping effect or the sparsity to group original data into clusters. This paper demonstrates the necessary of both the grouping effect and the sparsity for conducting subspace clustering, by proposing a new SelfRepresentation and Subspace Clustering based on Grouping Effect (SRGE) method. Specifically, first of all, a row sparse ' 2;1 -norm regularizer is utilized to represent each sample by other samples. Then, the grouping effect of the data is designed to ensure that the coefficient of close samples is similar, aiming at generating a diagonal block self-representation coefficient matrix. Finally, an affinity matrix is obtained for conducting spectral clustering. The proposed method can be regarded as a trade-off between SSC and LSR. The experimental results of segmentation on real datasets showed that the proposed method significantly outperformed the state-of-the-art methods in terms of all kinds of evaluation metrics.

Estimating equations under IPW imputation of missing data

“…The most restrictive one is the missing completely at random (MCAR), such as Zhang (2008b, Zhang et al ( , 2016, Zhang (2002a, 2002b). The less restrictive one is the MAR (missing at random), such as Zhang (2008a), Qin et al (2007) and Zhang et al (2017aZhang et al ( , 2017bZhang et al ( , 2018aZhang et al ( , 2018bZhang et al ( , 2018cZhang et al ( , 2010Zhang et al ( , 2005. The unrestrictive one is the missing not at random (MNAR).…”

Double-Norm Constrained Image Denoising Algorithm Based on Dictionary Learning Sparsity and FCM Structure Clustering

“…Then alternate direction multiplier method (ADMM) [20] and orthogonal matching pursuit (OMP) [21] method were applied to solve the optimization. The fuzzy c-means clustering algorithm [22]- [23] avoids the disadvantages of hard clustering and enhances the denoised image to retain more detailed information. Through the qualitative and quantitative comparison with similar excellent denoising algorithms, the effectiveness of the proposed algorithm is verified.…”