TY - GEN
T1 - Recent results on sparse principle component analysis
AU - Cai, T. Tony
AU - Ma, Zongming
AU - Wu, Yihong
PY - 2013
Y1 - 2013
N2 - Principal component analysis (PCA) is one of the most commonly used statistical procedures for dimension reduction. This paper presents some recent results on the minimax estimation of principal subspaces in high dimensions. Under mild technical conditions, we characterize the minimax risk for estimating the principal subspace under the quadratic loss within absolute constant factors.
AB - Principal component analysis (PCA) is one of the most commonly used statistical procedures for dimension reduction. This paper presents some recent results on the minimax estimation of principal subspaces in high dimensions. Under mild technical conditions, we characterize the minimax risk for estimating the principal subspace under the quadratic loss within absolute constant factors.
UR - http://www.scopus.com/inward/record.url?scp=84894221107&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894221107&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2013.6714037
DO - 10.1109/CAMSAP.2013.6714037
M3 - Conference contribution
AN - SCOPUS:84894221107
SN - 9781467331463
T3 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
SP - 181
EP - 183
BT - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
T2 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Y2 - 15 December 2013 through 18 December 2013
ER -