.png){kind=icon}
返回
Linear spectral hashing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2013.11.039.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Spectral hashing assigns binary hash keys to data points. This is accomplished via thresholding the eigenvectors of the graph Laplacian and obtaining binary codewords. While calculation for inputs in the training set is straightforward, an intriguing and difficult problem is how to compute the hash codewords for previously unseen data. For specific problems we propose linear scalar products as similarity measures and analyze the performance of the algorithm. We implement the linear algorithm and provide an inductive - generative - formula that leads to a codeword generation method similar to random hyperplane-based locality-sensitive hashing for a new data point. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Nearest-neighbor search
Spectral hashing
Spectral clustering
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
.png){kind=icon}
A Review of Technical Impact of Electrical Vehicle Charging Stations on Distribution Grid电动汽车充电站对配电网的技术影响研究综述
PREAI
分享收藏Mechanical characterization of neural electrodes based on PDMS-parylene C-PDMS sandwiched system
PREAI
分享收藏
分享收藏
查看更多内容