TY - GEN
T1 - Joint factor analysis and latent clustering
AU - Yang, Bo
AU - Fu, Xiao
AU - Sidiropoulos, Nicholas D.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Many real-life datasets exhibit structure in the form of physically meaningful clusters - e.g., news documents can be categorized as sports, politics, entertainment, and so on. Taking these clusters into account together with low-rank structure may yield parsimonious matrix and tensor factorization models and more powerful data analytics. Prior works made use of data-domain similarity to improve nonnegative matrix factorization. Here we are instead interested in joint low-rank factorization and latent-domain clustering; that is, in clustering the latent reduced-dimension representations of the observed entities. A unified algorithmic framework that can deal with both matrix and tensor factorization and latent clustering is proposed. Numerical results obtained from synthetic and real document data show that the proposed approach can significantly improve factor analysis and clustering accuracy.
AB - Many real-life datasets exhibit structure in the form of physically meaningful clusters - e.g., news documents can be categorized as sports, politics, entertainment, and so on. Taking these clusters into account together with low-rank structure may yield parsimonious matrix and tensor factorization models and more powerful data analytics. Prior works made use of data-domain similarity to improve nonnegative matrix factorization. Here we are instead interested in joint low-rank factorization and latent-domain clustering; that is, in clustering the latent reduced-dimension representations of the observed entities. A unified algorithmic framework that can deal with both matrix and tensor factorization and latent clustering is proposed. Numerical results obtained from synthetic and real document data show that the proposed approach can significantly improve factor analysis and clustering accuracy.
UR - http://www.scopus.com/inward/record.url?scp=84963799825&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963799825&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2015.7383764
DO - 10.1109/CAMSAP.2015.7383764
M3 - Conference contribution
AN - SCOPUS:84963799825
T3 - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
SP - 173
EP - 176
BT - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Y2 - 13 December 2015 through 16 December 2015
ER -