The k-sparse LSR for subspace clustering via 0-1 integer programming
T Yang, S Zhou, Z Zhang - Signal Processing, 2022 - Elsevier
Subspace clustering is a powerful technology for clustering high-dimensional data. Least
squares regression (LSR) is one of the classical subspace clustering models because of the …
squares regression (LSR) is one of the classical subspace clustering models because of the …
Double-Norm Constrained Image Denoising Algorithm Based on Dictionary Learning Sparsity and FCM Structure Clustering
C Ji, L He, W Dai - IEEE Access, 2022 - ieeexplore.ieee.org
To solve the problem of image smoothness and fuzzy edge texture information after image
denoising, proposed a new image denoising method based on dictionary learning. Firstly …
denoising, proposed a new image denoising method based on dictionary learning. Firstly …
Estimating equations under IPW imputation of missing data
H Wu, CC Li, C Cheng - International Journal of Reasoning …, 2021 - inderscienceonline.com
The inverse probability weighted (IPW) imputation method is first applied to compensate for
non-response. And then, the empirical likelihood (EL) inference is made for estimation …
non-response. And then, the empirical likelihood (EL) inference is made for estimation …
Efficient Subspace Clustering Based on Enhancing Local Structure and Global Structure
Q Yu, Y Zhang, C Sun - Advances in Intelligent Systems and Interactive …, 2020 - Springer
Subspace clustering (SC) achieves excellent clustering result via learning an affinity matrix
and applying the spectral clustering on the matrix in order to divide data points into several …
and applying the spectral clustering on the matrix in order to divide data points into several …