Learning binary codes with Bagging PCA

For the eigendecomposition based hashing approaches, the information caught in different dimensions is unbalanced and most of them is typically contained in the top eigenvectors. This often leads to an unexpected phenomenon that longer code does not necessarily yield better performance. This paper attempts to leverage the bootstrap sampling idea and integrate it with PCA, resulting in a new projection method called Bagging PCA, in order to learn effective binary codes. Specifically, a small fraction of the training data is randomly sampled to learn the PCA directions each time and only the top eigenvectors are kept to generate one piece of short code. This process is repeated several times and the obtained short codes are concatenated into one piece of long code. By considering each piece of short code as a "super-bit", the whole process is closely connected with the core idea of LSH. Both theoretical and experimental analyses demonstrate the effectiveness of the proposed method.