Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Table extraction using conditional random fields
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
Efficient projections onto the l1-ball for learning in high dimensions
Proceedings of the 25th international conference on Machine learning
A majorization-minimization algorithm for (multiple) hyperparameter learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Group lasso with overlap and graph lasso
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
MedLDA: maximum margin supervised topic models for regression and classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Exponential family sparse coding with applications to self-taught learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Distributed Algorithms for Topic Models
The Journal of Machine Learning Research
On smoothing and inference for topic models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Latent aspect rating analysis on review text data: a rating regression approach
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An architecture for parallel topic models
Proceedings of the VLDB Endowment
Aspect and sentiment unification model for online review analysis
Proceedings of the fourth ACM international conference on Web search and data mining
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Practical collapsed variational bayes inference for hierarchical dirichlet process
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Probabilistic topic models have shown remarkable success in many application domains. However, a probabilistic conditional topic model can be extremely inefficient when considering a rich set of features because it needs to define a normalized distribution, which usually involves a hard-to-compute partition function. This paper presents conditional topical coding (CTC), a novel formulation of conditional topic models which is non-probabilistic. CTC relaxes the normalization constraints as in probabilistic models and learns non-negative document codes and word codes. CTC does not need to define a normalized distribution and can efficiently incorporate a rich set of features for improved topic discovery and prediction tasks. Moreover, CTC can directly control the sparsity of inferred representations by using appropriate regularization. We develop an efficient and easy-to-implement coordinate descent learning algorithm, of which each coding substep has a closed-form solution. Finally, we demonstrate the advantages of CTC on online review analysis datasets. Our results show that conditional topical coding can achieve state-of-the-art prediction performance and is much more efficient in training (one order of magnitude faster) and testing (two orders of magnitude faster) than probabilistic conditional topic models.