@inproceedings{citeulike:1109893,
    author = {Wei, Xing and Croft, Bruce W.},
    booktitle = {SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference},
    citeulike-article-id = {1109893},
    citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1148204},
    citeulike-linkout-1 = {http://dx.doi.org/10.1145/1148170.1148204},
    doi = {10.1145/1148170.1148204},
    isbn = {1595933697},
    keywords = {information, models, retrieval, topic},
    posted-at = {2009-04-24 15:43:31},
    priority = {2},
    title = {LDA-based document models for ad-hoc retrieval},
    url = {http://dx.doi.org/10.1145/1148170.1148204},
    year = {2006}
}

@article{blei2009ncrp,
    abstract = {We present the nested Chinese restaurant process (nCRP), a stochastic process which assigns
probability distributions to innitely-deep, innitely-branching trees. We show how this stochastic
process can be used as a prior distribution in a Bayesian nonparametric model of document
collections. Specically, we present an application to information retrieval in which documents
are modeled as paths down a random tree, and the preferential attachment dynamics of the nCRP
leads to clustering of documents according to sharing of topics at multiple levels of abstraction.
Given a corpus of documents, a posterior inference algorithm nds an approximation to a posterior
distribution over trees, topics and allocations of words to levels of the tree. We demonstrate this
algorithm on collections of scientic abstracts from several journals. This model exemplies a
recent trend in statistical machine learning|the use of Bayesian nonparametric methods to infer
distributions on
exible data structures.},
    author = {Blei, David and Griffiths, Thomas and Jordan, Michael},
    citeulike-article-id = {5888264},
    journal = {Journal of the ACM},
    keywords = {blei, ncrp, non-parametric, topic-model},
    posted-at = {2009-10-05 06:53:03},
    priority = {2},
    title = {The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies},
    year = {2009}
}

@inproceedings{citeulike:432492,
  author = {David M. Blei and Thomas L. Griffiths and Michael I. Jordan and Joshua B. Tenenbaum},
  booktitle = {Advances in Neural Information Processing Systems},
  interHash = {f185b4657e25c733ee613bece516b3c5},
  intraHash = {13eceeb70da1669e302605e47d449a97},
  title = {Hierarchical Topic Models and
the Nested Chinese Restaurant Process},
  url = {http://www-psych.stanford.edu/~gruffydd/papers/ncrp.pdf},
  year = {2004},
  id = {432492},
  priority = {0},
  comment = {Learn a topic taxonomy with document assignments. They use Gibbs and a nested chinese restraurant prior. conceptually they learn the LDA + the hierarchy (the restaurants) but this is mixed in one posterior formular, where other variables than w_m,n c_m,l z_m,n and hyperparam eta are integrated out.

The chinese restaurant process gives the probability of sitting at an free (occupied) table in a chinese restaurant given the number of other customers.

The nested CRP uses the intiution of a culinary journey, where on each table in a restaurant there is a signpost to another restaurant, which the traveller follows. All travellers start at the same restaurant (the root node in the taxonomy) and follow the signpost on each table they sit during the journey.

Criticism: They have employed their experiments on very limited data 100 docs, with 100 words and 100 vocaulary size.},
  abstract = {We address the problem of learning topic hierarchies from data. The
model selection problem in this domain is dauntingÑwhich of the large
collection of possible trees to use? We take a Bayesian approach, generating
an appropriate prior via a distribution on partitions that we refer
to as the nested Chinese restaurant process. This nonparametric prior allows
arbitrarily large branching factors and readily accommodates growing
data collections. We build a hierarchical topic model by combining
this prior with a likelihood that is based on a hierarchical variant of latent
Dirichlet allocation. We illustrate our approach on simulated data and
with an application to the modeling of NIPS abstracts.}
}

@article{Blei03a,
  author = {D. Blei and A. Ng and M. Jordan},
  interHash = {9d1b808272b9e511425cbf557571e59a},
  intraHash = {e6d86f2c400079c53cd3e9dbb243e42e},
  journal = {Journal of Machine learning Research},
  pages = {993--1022},
  title = {Latent Dirichlet allocation},
  year = {2003}
}

@article{citeulike:117535,
    abstract = {A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.},
    author = {Dempster, A. P. and Laird, N. M. and Rubin, D. B.},
    citeulike-article-id = {117535},
    citeulike-linkout-0 = {http://dx.doi.org/10.2307/2984875},
    citeulike-linkout-1 = {http://www.jstor.org/stable/2984875},
    doi = {10.2307/2984875},
    issn = {00359246},
    journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
    number = {1},
    pages = {1--38},
    posted-at = {2009-09-17 19:23:37},
    priority = {2},
    publisher = {Blackwell Publishing for the Royal Statistical Society},
    title = {Maximum Likelihood from Incomplete Data via the EM Algorithm},
    url = {http://dx.doi.org/10.2307/2984875},
    volume = {39},
    year = {1977}
}

@article{journals/csda/TougasS07,
  author = {Jane Tougas and Raymond J. Spiteri},
  interHash = {5dfb83c90f911d408c098f42a779d735},
  intraHash = {696d3e39045d9496e9d8347728a24781},
  journal = {Computational Statistics \& Data Analysis},
  number = {1},
  pages = {174-183},
  title = {Updating the partial singular value decomposition in latent semantic indexing.},
  url = {http://dblp.uni-trier.de/db/journals/csda/csda52.html#TougasS07},
  volume = {52},
  year = {2007},
  ee = {http://dx.doi.org/10.1016/j.csda.2006.12.018},
  date = {2008-03-10}
}

@book{citeulike:163522,
    author = {Strang, Gilbert},
    citeulike-article-id = {163522},
    citeulike-linkout-0 = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&amp;path=ASIN/0961408898},
    citeulike-linkout-1 = {http://www.amazon.de/exec/obidos/redirect?tag=citeulike01-21&amp;path=ASIN/0961408898},
    citeulike-linkout-2 = {http://www.amazon.fr/exec/obidos/redirect?tag=citeulike06-21&amp;path=ASIN/0961408898},
    citeulike-linkout-3 = {http://www.amazon.co.uk/exec/obidos/ASIN/0961408898/citeulike00-21},
    citeulike-linkout-4 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0961408898},
    citeulike-linkout-5 = {http://www.worldcat.org/isbn/0961408898},
    citeulike-linkout-6 = {http://books.google.com/books?vid=ISBN0961408898},
    citeulike-linkout-7 = {http://www.amazon.com/gp/search?keywords=0961408898&index=books&linkCode=qs},
    citeulike-linkout-8 = {http://www.librarything.com/isbn/0961408898},
    howpublished = {Hardcover},
    isbn = {0961408898},
    keywords = {linear-algebra, math},
    posted-at = {2005-04-18 15:09:42},
    priority = {0},
    publisher = {{Wellesley Cambridge Press}},
    title = {Introduction to Linear Algebra, Fourth Edition},
    url = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0961408898},
    year = {2009}
}

@article{citeulike:78280,
    abstract = {A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (\&quot;semantic structure\&quot;) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca 100 orthogonal factors from which the original matrix can be...},
    author = {Deerwester, Scott C. and Dumais, Susan T. and Landauer, Thomas K. and Furnas, George W. and Harshman, Richard A.},
    citeulike-article-id = {78280},
    citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.49.7546},
    journal = {Journal of the American Society of Information Science},
    keywords = {indexing, latent, semantic},
    number = {6},
    pages = {391--407},
    posted-at = {2006-05-30 12:50:26},
    priority = {2},
    title = {Indexing by Latent Semantic Analysis},
    url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.49.7546},
    volume = {41},
    year = {1990}
}

@article{citeulike:1147881,
    abstract = {This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed technique uses a generative latent class model to perform a probabilistic mixture decomposition. This results in a more principled approach with a solid foundation in statistical inference. More precisely, we propose to make use of a temperature controlled version of the Expectation Maximization algorithm for model fitting, which has shown excellent performance in practice. Probabilistic Latent Semantic Analysis has many applications, most prominently in information retrieval, natural language processing, machine learning from text, and in related areas. The paper presents perplexity results for different types of text and linguistic data collections and discusses an application in automated document indexing. The experiments indicate substantial and consistent improvements of the probabilistic method over standard Latent Semantic Analysis.},
    author = {Hofmann, Thomas},
    citeulike-article-id = {1147881},
    citeulike-linkout-0 = {http://dx.doi.org/10.1023/A:1007617005950},
    citeulike-linkout-1 = {http://www.springerlink.com/content/l5656365840672g8},
    doi = {10.1023/A:1007617005950},
    journal = {Machine Learning},
    keywords = {learning, lsa, unsupervised},
    month = {January},
    number = {1},
    pages = {177--196},
    posted-at = {2008-01-17 08:47:14},
    priority = {2},
    title = {Unsupervised Learning by Probabilistic Latent Semantic Analysis},
    url = {http://dx.doi.org/10.1023/A:1007617005950},
    volume = {42},
    year = {2001}
}

@inproceedings{SIGIR:1999:hofmann,
    author = {Hofmann, Thomas},
    booktitle = {SIGIR '99: Proceedings of the 22nd annual international ACM SIGIR conference},
    citeulike-article-id = {110149},
    citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=312649},
    citeulike-linkout-1 = {http://dx.doi.org/10.1145/312624.312649},
    doi = {10.1145/312624.312649},
    isbn = {1581130961},
    keywords = {lda, lsa, topic\_models},
    pages = {50--57},
    posted-at = {2008-09-11 07:16:00},
    priority = {4},
    publisher = {ACM Press},
    title = {Probabilistic latent semantic indexing},
    url = {http://dx.doi.org/10.1145/312624.312649},
    year = {1999}
}

@inproceedings{SIGIR:2003:girolami,
    abstract = {Latent Dirichlet Allocation (LDA) is a fully generative approach to language modelling which overcomes the inconsistent generative semantics of Probabilistic Latent Semantic Indexing (PLSI). This paper shows that PLSI is a  maximum a posteriori  estimated LDA model under a uniform Dirichlet prior, therefore the perceived shortcomings of PLSI can be resolved and elucidated within the LDA framework.},
    author = {Girolami, Mark and Kab\'{a}n, Ata},
    booktitle = {SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference},
    citeulike-article-id = {675036},
    citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=860537},
    citeulike-linkout-1 = {http://dx.doi.org/10.1145/860435.860537},
    doi = {10.1145/860435.860537},
    isbn = {1-58113-646-3},
    keywords = {lda, plsi, topic\_models},
    pages = {433--434},
    posted-at = {2008-07-02 18:26:47},
    priority = {2},
    publisher = {ACM},
    title = {On an equivalence between PLSI and LDA},
    url = {http://dx.doi.org/10.1145/860435.860537},
    year = {2003}
}

@article{griffiths_finding_2004,
  author = {T. L. Griffiths and M. Steyvers},
  interHash = {387a5060792d52ea73b02dd68e52559e},
  intraHash = {d64890923898eb2adfed5173528f9563},
  journal = {Proceedings of the National Academy of Sciences},
  pages = {5228--5235},
  title = {Finding scientific topics},
  url = {http://www.pnas.org/cgi/reprint/0307752101v1.pdf},
  volume = {101},
  year = {2004}
}

@techreport{citeulike:3057616,
    abstract = {Abstract. Presents parameter estimation methods common with discrete proba-bility distributions, which is of particular interest in text modeling. Starting withmaximum likelihood, a posteriori and Bayesian estimation, central concepts likeconjugate distributions and Bayesian networks are reviewed. As an application,the model of latent Dirichlet allocation (LDA) is explained in detail with a fullderivation of an approximate inference algorithm based on Gibbs sampling, in-cluding a discussion of Dirichlet hyperparameter estimation.},
    author = {Heinrich, Gregor},
    citeulike-article-id = {3057616},
    citeulike-linkout-0 = {http://www.arbylon.net/publications/text-est.pdf},
    howpublished = {http://www.arbylon.net/publications/text-est.pdf},
    institution = {University of Leipzig},
    keywords = {lda, nlp},
    posted-at = {2009-05-31 23:11:09},
    priority = {2},
    title = {Parameter Estimation for Text Analysis},
    url = {http://www.arbylon.net/publications/text-est.pdf},
    year = {2008}
}

@article{wikipediaxml:2005,
author = {Ludovic Denoyer and Patrick Gallinari},
title = {{T}he {W}ikipedia {X}{M}{L} {C}orpus},
journal = {SIGIR Forum},
year = {2006}
}
