proposed algorithm. As with existing methods, PCA basis is tentatively used as dimension reduction for PRH and SRR. Note that it does not keep sparsity of transformation. Fig \[experiment:sift1m\_reduct\] shows the results of SIFT1M. It is clear that the proposed algorithm maintains higher performance with each dimension reduction compared to the other methods.

### High-dimensional Case

[c]{}

![Top-10 NN retrieval results for 64000 (**Upper**) and 25600-dimensional (**Lower**) VLAD data. The meaning of PRH(m, n, $\lambda$) is shown in Figure \[experiment:gauss\]. \[fig:vlad64000\]](./eps/graph_VLAD_64000.pdf "fig:") (a) comparison with baseline method.

![Top-10 NN retrieval results for 64000 (**Upper**) and 25600-dimensional (**Lower**) VLAD data. The meaning of PRH(m, n, $\lambda$) is shown in Figure \[experiment:gauss\]. \[fig:vlad64000\]](./eps/graph_VLAD_64000_lambdas.pdf "fig:") (b) effect of PCAT. $\qquad \qquad \qquad$

![Top-10 NN retrieval results for 64000 (**Upper**) and 25600-dimensional (**Lower**) VLAD data. The meaning of PRH(m, n, $\lambda$) is shown in Figure \[experiment:gauss\]. \[fig:vlad64000\]](./eps/graph_VLAD_64000_rpcas.pdf "fig:") (c) effect of RSPCA. $\qquad \qquad \qquad$

[c]{}

![Top-10 NN retrieval results for 64000 (**Upper**) and 25600-dimensional (**Lower**) VLAD data. The meaning of PRH(m, n, $\lambda$) is shown in Figure \[experiment:gauss\]. \[fig:vlad64000\]](./eps/graph_VLAD_25600.pdf "fig:") (d) comparison with baseline method.

![Top-10 NN retrieval results for 64000 (**Upper**) and 25600-dimensional (**Lower**) VLAD data. The meaning of PRH(m, n, $\lambda$) 