Abstract: In this paper, we propose a sparse coding algorithm based on matrix rank minimization and k-means clustering and for recognition.

Abstract—In this paper, we propose a sparse coding algorithm based on matrix rank minimization and k-means clustering and for recognition.

Long Ma, Chunheng Wang, Baihua Xiao: Sparse representation based on matrix rank minimization and k-means clustering for recognition. IJCNN 2012: 1-8.

Dictionary (also called vocabulary) learning is the key step here. One standard version of vocabulary learning is K-means clustering on image patches combined.

Jan 15, 2020 · This paper proposes a low-rank matrix decomposition non-convex optimization extended model without nuclear norm.

K-means clustering is a well known method that tries to minimize the sum of squared distances between each data point and its own cluster center. K-means has ...

In this paper, we propose a discriminative low-rank dictionary learning algorithm for sparse representation. Sparse representation seeks the sparsest ...

We propose a novel method based on a low-rank representation model, called KGLRR, that combines the low-rank representation approach with K-means clustering.

Dec 15, 2021 · In low-rank sparse representation theory, a noisy or missing data matrix is decomposed into an accurate data matrix and a singular/sparse data ...

We propose a method based on sparse representation. (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a high- ...