M-PCA Binary Embedding For Approximate Nearest Neighbor Search

Principal Component Analysis (PCA) is widely used within binary embedding methods for approximate nearest neighbor search and has proven to have a significant effect on the performance. Current methods aim to represent the whole data using a single PCA however, considering the Gaussian distribution requirements of PCA, this representation is not appropriate. In this study we propose using Multiple PCA (MPCA) transformations to represent the whole data and show that it increases the performance significantly compared to methods using a single PCA.