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Isotropic Hashing
Weihao Kong, WuJun Li
(2012 NIPS)
2015 3.19
![](https://csdnimg.cn/release/download_crawler_static/8759105/bg2.jpg)
Introduction
Low-dimensional case Linear search
High-dimensional case Hash search
Curse of dimensionality
1 、 Fast query speed
2 、 Low storage cost
![](https://csdnimg.cn/release/download_crawler_static/8759105/bg3.jpg)
Contributions
1 、 Hashing with isotropic variances for all di
mensions
2 、 Verify hashing with isotropic variances bett
er than hash with anisotropic variances.
![](https://csdnimg.cn/release/download_crawler_static/8759105/bg4.jpg)
Hash
Projection Stage (Dimension Reduction)
·Projected with real-valued projection function
·Given a point x, each projected dimension i will be associated with
a real-valued projection function
Quantization Stage
·Turn real into binary
)xwxfgexf
T
ii
)(..)((
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