科技报告详细信息
Asymmetric sparse kernel approximations for nearest neighbors search | |
Damek Davis ; Jonathan Balzer ; Stefano Soatto | |
UCLA Henry Samueli School of Engineering and Applied Science | |
RP-ID : 130009 | |
学科分类:计算机科学(综合) | |
美国|英语 | |
来源: UCLA Computer Science Technical Reports Database | |
【 摘 要 】
We introduce an asymmetric sparse approximate embedding optimized for fast kernel comparison. In contrast to other methods that perform an explicit approximate embedding using kernel PCA followed by a distance compression technique in R^d, which loses information at both steps, our method utilizes the implicit kernel representation directly. In addition, we empirically demonstrate that our method needs no training step and can operate with a dictionary of random exemplars from the dataset. We evaluate our method on three databases used in large-scale image retrieval.
【 预 览 】
Files | Size | Format | View |
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RO201804090001235LZ | 1007KB | download |