LDA的博客以及代码

1、Blei的LDA代码(C):http://www.cs.princeton.edu/~blei/lda-c/index.html
2、D.Bei的主页:http://www.cs.princeton.edu/~blei/publications.html
3、Gibbs LDA++ by Xuan-Hieu Phan and Cam-Tu Nguyen(C++):http://gibbslda.sourceforge.net/
4、用GibbsLDA做Topic Modeling (教程 by Lu Heng):http://weblab.com.cityu.edu.hk/blog/luheng/2011/06/
5、Daichi Mochihashi(C,Matlab) :http://chasen.org/~daiti-m/dist/lda/
6、Griffiths和Steyvers的Topic Modeling工具箱:http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm
7.shuyo的LDA(python):https://shuyo.wordpress.com/2011/05/18/latent-dirichlet-allocation-in-python/
8.python库(lda 1.0.2):https://pypi.python.org/pypi/lda
9.图像主题方面的LDA实现(python):http://www.mblondel.org/journal/2010/08/21/latent-dirichlet-allocation-in-python/
10、一些博客:
(1)【转】LDA必读的资料 http://www.xperseverance.net/blogs/2012/03/657/
(2)我爱机器学习小站里面LDA部分:http://www.52ml.net/tags/lda/page/7
(3)基于LDA的Topic Model变形 http://www.cnblogs.com/wentingtu/archive/2013/06/02/3113422.html
(4)Topic modeling LDA by Blei(对Blei关于LDA的一些文章的评论)http://blog.csdn.net/pirage/article/details/8889951
(5)Latent dirichlet allocation note(里面有对Daichi Mochihashi写的LDA代码应用的教程)http://blog.csdn.net/wangran51/article/details/7408399
(6)Blei教学LDA视频:http://videolectures.net/mlss09uk_blei_tm/

主题模型及其变种的实现代码汇总
1.MALLET:实现语言,Java,实现模型,LDA,HLDA,Pachinko Allocation Model,此外,还实现了HMM,最大熵马尔科夫模型和条件随机场;
2.Shuyo的github代码:实现语言,Python,实现模型,LDA,Dirichlet Process Gaussian Mixture Model,online HDP,HDPLDA,Interactive Topic Model,Labeled LDA
地址:https://github.com/shuyo/iir/tree/master/lda
3.arongdari的github代码:实现语言,Python,实现模型,LDA,Correlated Topic Model,Relational topic model,Author-Topic model,HMM-LDA,Discrete Infinite logistic normal,Supervised Topic Model,Hierarchical Dirichlet process,Hierarchical Dirichlet scaling process
地址:https://github.com/arongdari/python-topic-model
4.Gensim:实现语言,Python,实现模型,LDA,Dynamic Topic Model,Dynamic Influence Model,HDP,LSI,Random Projections,深度学习的word2vec,paragraph2vec。
官网地址:http://radimrehurek.com/gensim/index.html
github代码地址:https://github.com/piskvorky/gensim
5.ahmaurya的github代码:实现语言,Python,实现模型,Topic Over Time
github代码地址:https://github.com/ahmaurya/topics_over_time
6.Blei实验室的代码:实现语言,Python,实现模型,online lda,online HDP,turbo topic model,topic model visualization engine,实现语言,C,实现模型,correlated topic model,discrete infinite logistic normal,HLDA,lda,实现语言C++,实现模型,ctr,class-slda,Dynamic Topic model and the influence model,实现语言R,实现模型 lda
github代码地址:http://www.cs.columbia.edu/~blei/topicmodeling_software.html
7.中国科学技术信息研究所徐硕老师的PDF,对LDA,TOT,AT模型如何使用gibbs sampling求参进行了细致推导,并依据求参结果给出伪代码。
地址:http://blog.sciencenet.cn/blog-611051-582492.html

[转]LDA的必读文章和相关代码
LDA和HLDA:
(1)D. M. Blei, et al., “Latent Dirichlet allocation,” Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
(2)T. L. Griffiths and M. Steyvers, “Finding scientific topics,” Proceedings of the National Academy of Sciences, vol. 101, pp. 5228-5235, 2004.
(3)D. M. Blei, et al., “Hierarchical Topic Models and the Nested Chinese Restaurant Process,” NIPS, 2003.
(4)Blei的LDA视频教程:http://videolectures.net/mlss09uk_blei_tm/
(5)Teh的关于Dirichlet Processes的视频教程:http://videolectures.net/mlss07_teh_dp/
(6)Blei的毕业论文:http://www.cs.princeton.edu/~blei/papers/Blei2004.pdf
(7)Jordan的报告:http://www.icms.org.uk/downloads/mixtures/jordan_talk.pdf
(8)G. Heinrich, “Parameter Estimation for Text Analysis,” http://www.arbylon.net/publications/text-est.pdf
基础知识:
(1)P. Johnson and M. Beverlin, “Beta Distribution,” http://pj.freefaculty.org/ps707/Distributions/Beta.pdf
(2)M. Beverlin and P. Johnson, “The Dirichlet Family,” http://pj.freefaculty.org/stat/Distributions/Dirichlet.pdf
(3)P. Johnson, “Conjugate Prior and Mixture Distributions”,http://pj.freefaculty.org/stat/TimeSeries/ConjugateDistributions.pdf
(4)P.J. Green, “Colouring and Breaking Sticks:Random Distributions and Heterogeneous Clustering”,http://www.maths.bris.ac.uk/~mapjg/papers/GreenCDP.pdf
(5)Y. W. Teh, “Dirichlet Process”, http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/dp.pdf
(6)Y. W. Teh and M. I. Jordan, “Hierarchical Bayesian Nonparametric Models with Applications,”
http://www.stat.berkeley.edu/tech-reports/770.pdf
(7)T. P. Minka, “Estimating a Dirichlet Distribution”, http://research.microsoft.com/en-us/um/people/minka/papers/dirichlet/minka-dirichlet.pdf
(8)北邮论坛的LDA导读:[导读]文本处理、图像标注中的一篇重要论文Latent Dirichlet Allocation,http://bbs.byr.edu.cn/article/PR_AI/2530?p=1
(9)Zhou Li的LDA Note:http://lsa-lda.googlecode.com/files/Latent Dirichlet Allocation note.pdf
(10)C. M. Bishop, “Pattern Recognition And Machine Learning,” Springer, 2006.
代码:
(1)Blei的LDA代码(C):http://www.cs.princeton.edu/~blei/lda-c/index.html
(2)BLei的HLDA代码(C):http://www.cs.princeton.edu/~blei/downloads/hlda-c.tgz
(3)Gibbs LDA(C++):http://gibbslda.sourceforge.net/
(4)Delta LDA(Python):http://pages.cs.wisc.edu/~andrzeje/research/deltaLDA.tgz
(5)Griffiths和Steyvers的Topic Modeling工具箱:http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm
(6)LDA(Java):http://www.arbylon.net/projects/
(7)Mochihashi的LDA(C,Matlab):http://chasen.org/~daiti-m/dist/lda/
(8)Chua的LDA(C#):http://www.mysmu.edu/phdis2009/freddy.chua.2009/programs/lda.zip
(9)Chua的HLDA(C#):http://www.mysmu.edu/phdis2009/freddy.chua.2009/programs/hlda.zip
其他:
(1)S. Geman and D. Geman, “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. PAMI-6, pp. 721-741, 1984.
(2)B. C. Russell, et al., “Using Multiple Segmentations to Discover Objects and their Extent in Image Collections,” in Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, 2006, pp. 1605-1614.
(3)J. Sivic, et al., “Discovering objects and their location in images,” in Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, 2005, pp. 370-377 Vol. 1.
(4)F. C. T. Chua, “Summarizing Amazon Reviews using Hierarchical Clustering,”http://www.mysmu.edu/phdis2009/freddy.chua.2009/papers/amazon.pdf
(5)F. C. T. Chua, “Dimensionality Reduction and Clustering of Text Documents,”http://www.mysmu.edu/phdis2009/freddy.chua.2009/papers/probabilisticIR.pdf
(6)D Bacciu, “Probabilistic Generative Models for Machine Vision,”http://www.math.unipd.it/~sperduti/AI09/bacciu_unipd_handouts.pdf

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