213 research outputs found
Temporal Topic Analysis with Endogenous and Exogenous Processes
We consider the problem of modeling temporal textual data taking endogenous
and exogenous processes into account. Such text documents arise in real world
applications, including job advertisements and economic news articles, which
are influenced by the fluctuations of the general economy. We propose a
hierarchical Bayesian topic model which imposes a "group-correlated"
hierarchical structure on the evolution of topics over time incorporating both
processes, and show that this model can be estimated from Markov chain Monte
Carlo sampling methods. We further demonstrate that this model captures the
intrinsic relationships between the topic distribution and the time-dependent
factors, and compare its performance with latent Dirichlet allocation (LDA) and
two other related models. The model is applied to two collections of documents
to illustrate its empirical performance: online job advertisements from
DirectEmployers Association and journalists' postings on BusinessInsider.com
Iteratively Reweighted Least Squares Algorithms for L1-Norm Principal Component Analysis
Principal component analysis (PCA) is often used to reduce the dimension of
data by selecting a few orthonormal vectors that explain most of the variance
structure of the data. L1 PCA uses the L1 norm to measure error, whereas the
conventional PCA uses the L2 norm. For the L1 PCA problem minimizing the
fitting error of the reconstructed data, we propose an exact reweighted and an
approximate algorithm based on iteratively reweighted least squares. We provide
convergence analyses, and compare their performance against benchmark
algorithms in the literature. The computational experiment shows that the
proposed algorithms consistently perform best
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