TY - JOUR
T1 - An Effective Clustering Optimization Method for Unsupervised Linear Discriminant Analysis
AU - Wang, Quan
AU - Wang, Fei
AU - Ren, Fuji
AU - Li, Zhongheng
AU - Nie, Feiping
N1 - Publisher Copyright:
© 1989-2012 IEEE.
PY - 2023/4/1
Y1 - 2023/4/1
N2 - The recent work Unsupervised Linear Discriminant Analysis (Un-LDA) completes its clustering process during the alternating optimization by converting equivalently the objective and finally using the K-means algorithm. However, the K-means algorithm has its inherent drawbacks. It is hard for the K-means algorithm to deal well with some complex clustering cases where there are too many real clusters or non-convex clusters. In this paper, a novel clustering optimization method is presented to accomplish the clustering process in Un-LDA and the resulting method can be named Un-LDA(CD). Specifically, instead of the K-means algorithm, an elaborately designed coordinate descent algorithm is adopted to obtain the clusters after the objective function goes through a series of simple but deft equivalent conversions. Extensive experiments have demonstrated that the coordinate descent clustering solution for Un-LDA can outperform the original K-means based solution on the tested data sets especially those complex data sets with a pretty large number of real clusters.
AB - The recent work Unsupervised Linear Discriminant Analysis (Un-LDA) completes its clustering process during the alternating optimization by converting equivalently the objective and finally using the K-means algorithm. However, the K-means algorithm has its inherent drawbacks. It is hard for the K-means algorithm to deal well with some complex clustering cases where there are too many real clusters or non-convex clusters. In this paper, a novel clustering optimization method is presented to accomplish the clustering process in Un-LDA and the resulting method can be named Un-LDA(CD). Specifically, instead of the K-means algorithm, an elaborately designed coordinate descent algorithm is adopted to obtain the clusters after the objective function goes through a series of simple but deft equivalent conversions. Extensive experiments have demonstrated that the coordinate descent clustering solution for Un-LDA can outperform the original K-means based solution on the tested data sets especially those complex data sets with a pretty large number of real clusters.
KW - Clustering optimization method
KW - coordinate descent
KW - dimensionality reduction
KW - linear discriminant analysis
KW - unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85118642286&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2021.3124023
DO - 10.1109/TKDE.2021.3124023
M3 - 文章
AN - SCOPUS:85118642286
SN - 1041-4347
VL - 35
SP - 3444
EP - 3457
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 4
ER -