%A Trung Thanh Le
%A Abed Meraim Karim
%A Hafiane Adel
%A Linh Trung Nguyen
%T Sparse Subspace Tracking in High Dimensions
%X We studied the problem of sparse subspace tracking in the
high-dimensional regime where the dimension is comparable
to or much larger than the sample size. Leveraging power iteration and thresholding methods, a new provable algorithm
called OPIT was derived for tracking the sparse principal subspace of data streams over time. We also presented a theoretical result on its convergence to verify its consistency in high dimensions. Several experiments were carried out on both synthetic and real data to demonstrate the effectiveness of OPIT.
%C Singapore
%L SisLab4712