Pseudo-inverse locality preserving iterative hashing

Hashing learning has attracted increasing attention these years with the rapid increase of data. Some high-dimensional data have caused the ‘dimension disaster’ which make traditional methods ineffective. In this paper, we propose a method to find the nearest neighbor quickly from the high-dimensional data, named pseudo-inverse locality preserving iterative hashing(PLIH). We use pseudo-inverse to replace the inverse matrix in order to solve the problem of matrix singularity. We construct adjacency graphs and minimize the distance of the neighbors in the subspace to make the projected matrix maintain the neighborhood relations of high dimension, which solves the problem that the locality sensitive hashing cannot preserve the high-dimensional neighborhood relations effectively. Because different bit with different weight has more discriminating power than the same weight, Loss of the projection matrix in the quantization process is minimized by weighted iterative quantization. Experiments on public datasets Cnn_4096d_Caltech and Gist_512d_Caltech demonstrated that accuracy and recall of the PLIPH are both better than the traditional hashing algorithms.