TY - JOUR
T1 - Discriminative Projected Clustering via Unsupervised LDA
AU - Nie, Feiping
AU - Dong, Xia
AU - Hu, Zhanxuan
AU - Wang, Rong
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - This work focuses on the projected clustering problem. Specifically, an efficient and parameter-free clustering model, named discriminative projected clustering (DPC), is proposed for simultaneously low-dimensional and discriminative projection learning and clustering, from the perspective of least squares regression. The proposed DPC, a constrained regression model, aims at finding both a transformation matrix and a binary indicator matrix to minimize the sum-of-squares error. Theoretically, a significant conclusion is drawn and used to reveal the connection between DPC and linear discriminant analysis (LDA). Experimentally, experiments are conducted on both toy and real-world data to validate the effectiveness and efficiency of DPC; experiments are also conducted on hyperspectral images to further verify its practicability in real-world applications. Experimental results demonstrate that DPC achieves comparable or superior results to some state-of-the-art clustering methods.
AB - This work focuses on the projected clustering problem. Specifically, an efficient and parameter-free clustering model, named discriminative projected clustering (DPC), is proposed for simultaneously low-dimensional and discriminative projection learning and clustering, from the perspective of least squares regression. The proposed DPC, a constrained regression model, aims at finding both a transformation matrix and a binary indicator matrix to minimize the sum-of-squares error. Theoretically, a significant conclusion is drawn and used to reveal the connection between DPC and linear discriminant analysis (LDA). Experimentally, experiments are conducted on both toy and real-world data to validate the effectiveness and efficiency of DPC; experiments are also conducted on hyperspectral images to further verify its practicability in real-world applications. Experimental results demonstrate that DPC achieves comparable or superior results to some state-of-the-art clustering methods.
KW - Clustering
KW - hyperspectral image clustering
KW - least squares
KW - linear discriminant analysis (LDA)
KW - projection learning
UR - http://www.scopus.com/inward/record.url?scp=85139450996&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2022.3202719
DO - 10.1109/TNNLS.2022.3202719
M3 - 文章
C2 - 36121958
AN - SCOPUS:85139450996
SN - 2162-237X
VL - 34
SP - 9466
EP - 9480
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 11
ER -