An optimal locality preserving indexing algorithm for text mining

LPI is not efficient in time and memory which makes it difficult to be applied to very large data set. We propose a optimal algorithm called FLPI. FLPI decomposes the LPI problem as a graph embedding problem plus a regularized least squares problem. Such modification avoids eigen decomposition of dense matrices and can significantly reduce both time and memory cost in computation. Moreover, with a specifically designed graph in supervised situation, LPI only needs to solve the regularized least squares problem which is a further saving of time and memory. Real data experimental results show that FLPI obtains similar or better results comparing to LPI.