TY - JOUR
T1 - Robust weighted co-clustering with global and local discrimination
AU - Lu, Zhoumin
AU - Wang, Shiping
AU - Liu, Genggeng
AU - Nie, Feiping
N1 - Publisher Copyright:
© 2023
PY - 2023/6
Y1 - 2023/6
N2 - In the past few decades, the clustering problem has made considerable progress, and co-clustering algorithms have attracted more attention. Compared with one-side clustering, co-clustering not only groups samples according to the distribution of features but also groups features according to the distribution of samples at the same time. This duality helps to explore the structural information of data, such as genes and texts. In this paper, a new co-clustering algorithm is proposed to simultaneously consider feature weights, data noise, local manifolds, and global scatter, named robust weighted co-clustering with global and local discrimination. Furthermore, an alternate update rule is put forward to optimize objective, theoretically proven to converge. Then, the algorithm's duality, robustness, and effectiveness have been verified on synthetic, corrupted, and real datasets, respectively. The runtime and parameter sensitivity of the algorithm are also analyzed. Finally, sufficient experiments clarify the competitiveness of our algorithm compared to other ones.
AB - In the past few decades, the clustering problem has made considerable progress, and co-clustering algorithms have attracted more attention. Compared with one-side clustering, co-clustering not only groups samples according to the distribution of features but also groups features according to the distribution of samples at the same time. This duality helps to explore the structural information of data, such as genes and texts. In this paper, a new co-clustering algorithm is proposed to simultaneously consider feature weights, data noise, local manifolds, and global scatter, named robust weighted co-clustering with global and local discrimination. Furthermore, an alternate update rule is put forward to optimize objective, theoretically proven to converge. Then, the algorithm's duality, robustness, and effectiveness have been verified on synthetic, corrupted, and real datasets, respectively. The runtime and parameter sensitivity of the algorithm are also analyzed. Finally, sufficient experiments clarify the competitiveness of our algorithm compared to other ones.
KW - Co-clustering
KW - Global discrimination
KW - Local discrimination
KW - Machine learning
KW - Nonnegative matrix factorization
UR - http://www.scopus.com/inward/record.url?scp=85147717400&partnerID=8YFLogxK
U2 - 10.1016/j.patcog.2023.109405
DO - 10.1016/j.patcog.2023.109405
M3 - 文章
AN - SCOPUS:85147717400
SN - 0031-3203
VL - 138
JO - Pattern Recognition
JF - Pattern Recognition
M1 - 109405
ER -