메뉴 건너뛰기




Volumn , Issue , 2011, Pages 475-483

Conditional topical coding: An efficient topic model conditioned on rich features

Author keywords

Conditional models; Sparse coding; Topic models

Indexed keywords

CODES (SYMBOLS); DATA MINING; PROBABILITY DISTRIBUTIONS;

EID: 80052646861     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2020408.2020484     Document Type: Conference Paper
Times cited : (4)

References (42)
  • 1
    • 77954725706 scopus 로고    scopus 로고
    • On smoothing and inference for topic models
    • A. Asuncion, M. Welling, P. Smyth, and Y. Teh. On smoothing and inference for topic models. In UAI, 2009.
    • (2009) UAI
    • Asuncion, A.1    Welling, M.2    Smyth, P.3    Teh, Y.4
  • 3
    • 74549185029 scopus 로고    scopus 로고
    • Supervised topic models
    • D. Blei and J. McAuliffe. Supervised topic models. In NIPS, 2007.
    • (2007) NIPS
    • Blei, D.1    McAuliffe, J.2
  • 4
    • 0141607824 scopus 로고    scopus 로고
    • Latent Dirichlet allocation
    • D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. JMLR, (3):993-1022, 2003.
    • (2003) JMLR , Issue.3 , pp. 993-1022
    • Blei, D.1    Ng, A.2    Jordan, M.3
  • 5
    • 74549174511 scopus 로고    scopus 로고
    • Learning document-level semantic properties from free-text annotations
    • S. Branavan, H. Chen, J. Eisenstein, and R. Barzilay. Learning document-level semantic properties from free-text annotations. In ACL, 2008.
    • (2008) ACL
    • Branavan, S.1    Chen, H.2    Eisenstein, J.3    Barzilay, R.4
  • 7
    • 34547976903 scopus 로고    scopus 로고
    • Sparse multinomial logistic regression via bayesian l1 regularization
    • G. Cawley, N. Talbot, and M. Girolami. Sparse multinomial logistic regression via bayesian l1 regularization. In NIPS, 2007.
    • (2007) NIPS
    • Cawley, G.1    Talbot, N.2    Girolami, M.3
  • 9
    • 56449092085 scopus 로고    scopus 로고
    • Efficient projections onto the l1-ball for learning in high dimensions
    • J. Duchi, S. Shalev-Shwartz, Y. Singer, and T. Chandra. Efficient projections onto the l1-ball for learning in high dimensions. In ICML, 2008.
    • (2008) ICML
    • Duchi, J.1    Shalev-Shwartz, S.2    Singer, Y.3    Chandra, T.4
  • 10
    • 71249110320 scopus 로고    scopus 로고
    • A majorization-minimization algorithm for (multiple) hyperparameter learning
    • C.-S. Foo, C. Do, and A. Ng. A majorization-minimization algorithm for (multiple) hyperparameter learning. In ICML, 2009.
    • (2009) ICML
    • Foo, C.-S.1    Do, C.2    Ng, A.3
  • 11
    • 80053292525 scopus 로고    scopus 로고
    • Posterior vs. Parameter sparsity in latent variable models
    • J. Graca, K. Ganchev, B. Taskar, and F. Pereira. Posterior vs. parameter sparsity in latent variable models. In NIPS, 2009.
    • (2009) NIPS
    • Graca, J.1    Ganchev, K.2    Taskar, B.3    Pereira, F.4
  • 12
    • 85026972772 scopus 로고    scopus 로고
    • Probabilistic latent semantic analysis
    • T. Hofmann. Probabilistic latent semantic analysis. In UAI, 1999.
    • (1999) UAI
    • Hofmann, T.1
  • 14
    • 0348223982 scopus 로고    scopus 로고
    • Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation
    • A. Hyvärinen. Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation. Neural Computation, (11):1739U-1768, 1999.
    • (1999) Neural Computation , Issue.11
    • Hyvärinen, A.1
  • 15
    • 0034920427 scopus 로고    scopus 로고
    • A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images
    • DOI 10.1016/S0042-6989(01)00114-6, PII S0042698901001146
    • A. Hyvärinen and P. Hoyer. A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images. Vision Research, 41(18):2413-2423, 2001. (Pubitemid 32667541)
    • (2001) Vision Research , vol.41 , Issue.18 , pp. 2413-2423
    • Hyvarinen, A.1    Hoyer, P.O.2
  • 16
    • 71149113559 scopus 로고    scopus 로고
    • Group lasso with overlap and graph lasso
    • L. Jacob, G. Obozinski, and J.-P. Vert. Group lasso with overlap and graph lasso. In ICML, 2009.
    • (2009) ICML
    • Jacob, L.1    Obozinski, G.2    Vert, J.-P.3
  • 17
    • 77956506018 scopus 로고    scopus 로고
    • Proximal methods for sparse hierarchical dictionary learning
    • R. Jenatton, J. Mairal, G. Obozinski, and F. Bach. Proximal methods for sparse hierarchical dictionary learning. In ICML, 2010.
    • (2010) ICML
    • Jenatton, R.1    Mairal, J.2    Obozinski, G.3    Bach, F.4
  • 18
    • 79952432020 scopus 로고    scopus 로고
    • Aspect and sentiment unification model for online review analysis
    • Y. Jo and A. Oh. Aspect and sentiment unification model for online review analysis. In WSDM, 2011.
    • (2011) WSDM
    • Jo, Y.1    Oh, A.2
  • 19
    • 85162389868 scopus 로고    scopus 로고
    • Variational bounds for mixed-data factor analysis
    • M. E. Khan, B. Marlin, G. Bouchard, and K. Murphy. Variational bounds for mixed-data factor analysis. In NIPS, 2010.
    • (2010) NIPS
    • Khan, M.E.1    Marlin, B.2    Bouchard, G.3    Murphy, K.4
  • 20
    • 77956548668 scopus 로고    scopus 로고
    • Tree-guided group lasso for multi-task regression with structured sparsity
    • S. Kim and E. P. Xing. Tree-guided group lasso for multi-task regression with structured sparsity. In ICML, 2010.
    • (2010) ICML
    • Kim, S.1    Xing, E.P.2
  • 21
    • 0142192295 scopus 로고    scopus 로고
    • Conditional random fields: Probabilistic models for segmenting and labeling sequence data
    • J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, 2001.
    • (2001) ICML
    • Lafferty, J.1    McCallum, A.2    Pereira, F.3
  • 22
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • D. Lee and H. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401:788 - 791, 1999.
    • (1999) Nature , vol.401 , pp. 788-791
    • Lee, D.1    Seung, H.2
  • 23
    • 78751681286 scopus 로고    scopus 로고
    • Exponential family sparse coding with applications to self-taught learning
    • H. Lee, R. Raina, A. Teichman, and A. Ng. Exponential family sparse coding with applications to self-taught learning. In IJCAI, 2009.
    • (2009) IJCAI
    • Lee, H.1    Raina, R.2    Teichman, A.3    Ng, A.4
  • 25
    • 65349193793 scopus 로고    scopus 로고
    • Lasso-type recovery of sparse representations for high-dimensional data
    • N. Meinshausen and B. Yu. Lasso-type recovery of sparse representations for high-dimensional data. Annals of Statistics, 37(1):246-270, 2009.
    • (2009) Annals of Statistics , vol.37 , Issue.1 , pp. 246-270
    • Meinshausen, N.1    Yu, B.2
  • 26
    • 77951202623 scopus 로고    scopus 로고
    • Topic models conditioned on arbitrary features with dirichlet-multinomial regression
    • D. Mimno and A. McCallum. Topic models conditioned on arbitrary features with dirichlet-multinomial regression. In UAI, 2008.
    • (2008) UAI
    • Mimno, D.1    McCallum, A.2
  • 27
    • 70349433731 scopus 로고    scopus 로고
    • Distributed algorithms for topic models
    • D. Newman, A. Asuncion, P. Smyth, and M. Welling. Distributed algorithms for topic models. JMLR, (10):1801-1828, 2009.
    • (2009) JMLR , vol.10 , pp. 1801-1828
    • Newman, D.1    Asuncion, A.2    Smyth, P.3    Welling, M.4
  • 28
    • 0029938380 scopus 로고    scopus 로고
    • Emergence of simple-cell receptive field properties by learning a sparse code for natural images
    • DOI 10.1038/381607a0
    • B. Olshausen and D. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583):607-609, 1996. (Pubitemid 26177476)
    • (1996) Nature , vol.381 , Issue.6583 , pp. 607-609
    • Olshausen, B.A.1    Field, D.J.2
  • 30
    • 1542287488 scopus 로고    scopus 로고
    • Table extraction using conditional random fields
    • D. Pinto, A. McCallum, X. Wei, and W. Croft. Table extraction using conditional random fields. In SIGIR, 2003.
    • (2003) SIGIR
    • Pinto, D.1    McCallum, A.2    Wei, X.3    Croft, W.4
  • 31
    • 85046032701 scopus 로고    scopus 로고
    • Sparse overcomplete latent variable decomposition of counts data
    • M. Shashanka, B. Raj, and P. Smaragdis. Sparse overcomplete latent variable decomposition of counts data. In NIPS, 2007.
    • (2007) NIPS
    • Shashanka, M.1    Raj, B.2    Smaragdis, P.3
  • 32
    • 80052119994 scopus 로고    scopus 로고
    • An architecture for parallel topic models
    • A. Smola and S. Narayanamurthy. An architecture for parallel topic models. In VLDB, 2010.
    • (2010) VLDB
    • Smola, A.1    Narayanamurthy, S.2
  • 35
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • R. Tibshirani. Regression shrinkage and selection via the lasso. J. Royal. Statist. Soc., B(58):267-288, 1996.
    • (1996) J. Royal. Statist. Soc., B , Issue.58 , pp. 267-288
    • Tibshirani, R.1
  • 36
    • 84859906262 scopus 로고    scopus 로고
    • A joint model of text and aspect ratings for sentiment summarization
    • I. Titov and R. McDonald. A joint model of text and aspect ratings for sentiment summarization. In ACL, 2008.
    • (2008) ACL
    • Titov, I.1    McDonald, R.2
  • 37
    • 70450178502 scopus 로고    scopus 로고
    • Simultaneous image classification and annotation
    • C. Wang, D. Blei, and L. Fei-Fei. Simultaneous image classification and annotation. In CVPR, 2009.
    • (2009) CVPR
    • Wang, C.1    Blei, D.2    Fei-Fei, L.3
  • 38
    • 77956195200 scopus 로고    scopus 로고
    • Latent aspect rating analysis on review text data: A rating regression approach
    • H. Wang, Y. Lu, and C. Zhai. Latent aspect rating analysis on review text data: A rating regression approach. In KDD, 2010.
    • (2010) KDD
    • Wang, H.1    Lu, Y.2    Zhai, C.3
  • 39
    • 70450209196 scopus 로고    scopus 로고
    • Linear spatial pyramid matching using sparse coding for image classification
    • J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In CVPR, 2009.
    • (2009) CVPR
    • Yang, J.1    Yu, K.2    Gong, Y.3    Huang, T.4
  • 40
    • 71149117321 scopus 로고    scopus 로고
    • MedLDA: Maximum margin supervised topic models for regression and classification
    • J. Zhu, A. Ahmed, and E. P. Xing. MedLDA: Maximum margin supervised topic models for regression and classification. In ICML, 2009.
    • (2009) ICML
    • Zhu, J.1    Ahmed, A.2    Xing, E.P.3
  • 41
    • 77956530160 scopus 로고    scopus 로고
    • Conditional topic random fields
    • J. Zhu and E. P. Xing. Conditional topic random fields. In ICML, 2010.
    • (2010) ICML
    • Zhu, J.1    Xing, E.P.2
  • 42
    • 80053134572 scopus 로고    scopus 로고
    • Sparse topical coding
    • J. Zhu and E. P. Xing. Sparse topical coding. In UAI, 2011.
    • (2011) UAI
    • Zhu, J.1    Xing, E.P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.