Hashing Algorithms for Approximate Nearest Neighbor Search
=============
We provide here the matlab codes of some popular hashing algorithms. Most of the core functions are written by the author of the original paper, we simply add some wrapper codes for the interface.
These codes are used in our paper: [A Revisit of Hashing Algorithms for Approximate Nearest Neighbor Search](http://arxiv.org/abs/1612.07545)
Currently, the following hashing algorithms are included:
* LSH (Locality Sensitive Hashing) [1] [2]
* SH (Spectral Hashing) [3]
* KLSH (Kernelized Locality Sensitive Hashing) [4]
* BRE (Binary Reconstructive Embeddings) [5]
* USPLH (Unsupervised Sequential Projection Learning Hashing) [6]
* ITQ (Iterative Quantization) [7]
* AGH (Anchor Graph Hashing) [8]
* SpH (Spherical Hashing) [9]
* IsoH (Isotropic Hashing) [10]
* CH (Compressed Hashing) [11]
* CPH (Complementary Projection Hashing) [12]
* HamH (Harmonious Hashing) [13]
* DSH (Density Sensitive Hashing) [14]
If you have a new hashing algorithm and want to share with us, welcome to send the matlab code to me and I will include it in this package.
All these hashing algorithms are in the **Unsupervised** folder
* The *HashingRunLongCode.m* is used to call a specified hashing algorithm (eg, LSH, SH, BRE ...) to generate the binary code of the base set and the query set, and the long code will be stored in multiple 32-bits tables for convenience.
* The *GenKmeansPartitions.m* is used to generate kmeans partitions of the data.
The binary format code file and the partition file can be used as the hash index of the [Search with Hash Index](https://github.com/ZJULearning/hashingSearch) algorithm.
If you have some problems or find some bugs in the codes, please email: dengcai AT gmail DOT com
1. Aristides Gionis, Piotr Indyk, Rajeev Motwani: Similarity Search in High Dimensions via Hashing. VLDB 1999: 518-529
2. Moses S. Charikar: Similarity estimation techniques from rounding algorithms. Proceedings of the thiry-fourth annual ACM symposium on Theory of computing, 2002.
3. Yair Weiss, Antonio Torralba, Robert Fergus: Spectral Hashing. NIPS 2008: 1753-1760
4. Brian Kulis, Kristen Grauman: Kernelized locality-sensitive hashing for scalable image search. 2130-2137, ICCV 2009
5. Brian Kulis, Trevor Darrell: Learning to Hash with Binary Reconstructive Embeddings. NIPS 2009: 1042-1050
6. Jun Wang, Sanjiv Kumar, Shih-Fu Chang: Sequential Projection Learning for Hashing with Compact Codes. ICML 1127-1134, 2010
7. Yunchao Gong, Svetlana Lazebnik: Iterative quantization: A procrustean approach to learning binary codes. CVPR 2011
8. Wei Liu, Jun Wang, Sanjiv Kumar, Shih-Fu Chang: Hashing with Graphs. ICML 2011
9. Jae-Pil Heo, Youngwoon Lee, Junfeng He, Shih-Fu Chang, Sung-Eui Yoon: Spherical hashing. CVPR 2012
10. Weihao Kong, Wu-Jun Li: Isotropic Hashing. NIPS 1655-1663 2012
11. Yue Lin, Rong Jin, Deng Cai, Shuicheng Yan, Xuelong Li: Compressed Hashing. CVPR 2013
12. Zhongming Jin, Yao Hu, Yue Lin, Debing Zhang, Shiding Lin, Deng Cai, Xuelong Li: Complementary Projection Hashing. 257-264 ICCV 2013
13. Bin Xu, Jiajun Bu, Yue Lin, Chun Chen, Xiaofei He, Deng Cai: Harmonious Hashing. 1820-1826 IJCAI 2013
14. Zhongming Jin, Cheng Li, Yue Lin, Deng Cai: Density Sensitive Hashing. IEEE Trans. Cybernetics 44(8): 1362-1371 (2014)
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Matlab codes for feature learning.zip
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Matlab codes for feature learning.zip (147个子文件)
LeastR.m 20KB
constructW.m 19KB
litekmeans.m 16KB
GNMF_KL_Multi.m 14KB
lsqr2.m 13KB
SR.m 12KB
hungarian.m 12KB
lars.m 10KB
LapGMM.m 10KB
columnlegend.m 10KB
SR_caller.m 9KB
KSR_caller.m 9KB
LGE.m 9KB
OLGE.m 9KB
LDA.m 8KB
SRKDAtrain.m 8KB
LapPLSI.m 8KB
MCFS_p.m 8KB
LCGMM.m 8KB
KSR.m 8KB
learn_coefficients.m 8KB
SRDAtrain.m 7KB
GNMF_Multi.m 7KB
IsoP.m 7KB
NPE.m 7KB
LCCF_Multi.m 6KB
KGE.m 6KB
LTM.m 6KB
TensorLGE.m 6KB
sll_opts.m 5KB
KDA.m 5KB
HashingRunLongCode.m 5KB
UKSRtrain.m 5KB
LSDA.m 5KB
USRtrain.m 5KB
SDA.m 5KB
CSRKDAtrain.m 5KB
LPP.m 5KB
MMP.m 5KB
trainUSPLH.m 5KB
SCC.m 4KB
BRE.m 4KB
GraphSC.m 4KB
OLPP.m 4KB
KLPP.m 4KB
TensorLPP.m 4KB
LIsomap.m 4KB
SRKDApredict.m 4KB
ContW.m 4KB
CPH.m 4KB
mySVD.m 4KB
KPCA.m 3KB
SparseCodingwithBasis.m 3KB
GNMF_KL.m 3KB
LSC.m 3KB
SDNMF_Multi.m 3KB
MAED.m 3KB
EMR.m 3KB
getargs.m 3KB
SCCtest.m 3KB
SRKDAtest.m 3KB
TwoLayerAGH_Train.m 3KB
LaplacianScore.m 3KB
Eigenmap.m 3KB
constructKernel.m 3KB
UKSRtest.m 3KB
initFactor.m 3KB
GNMF.m 2KB
Isomap.m 2KB
DSH_learn.m 2KB
SDNMF.m 2KB
SRDApredict.m 2KB
learn_basis.m 2KB
CSRKDApredict.m 2KB
OneLayerAGH_Train.m 2KB
SphericalHashing.m 2KB
LCCF.m 2KB
genpathGIT.m 2KB
PCA.m 2KB
USRtest.m 2KB
TwoLayerAGH_Test.m 2KB
CH_learn.m 2KB
HamH_learn.m 2KB
NormalizeFea.m 2KB
KLSH_learn.m 2KB
OneLayerAGH_Test.m 2KB
SpectralHashing.m 1KB
SRDAtest.m 1KB
EMRtest.m 1KB
IsoH_learn.m 1KB
MAEDseq.m 1KB
GenKmeansPartitions.m 1KB
fvecs_read.m 1KB
ivecs_read.m 1KB
AGH2_learn.m 1KB
BRE_learn.m 1KB
CH_compress.m 1KB
AGH1_learn.m 1KB
tfidf.m 1KB
CPH_learn.m 1KB
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