We propose a semi-parametric and dynamic rank factor model for topic model-ing, capable of (i) discovering topic prevalence over time, and (ii) learning con-temporary multi-scale dependence structures, providing topic and word correla-tions as a byproduct. The high-dimensional and time-evolving ordinal/rank ob-servations (such as word counts), after an arbitrary monotone transformation, are well accommodated through an underlying dynamic sparse factor model. The framework naturally admits heavy-tailed innovations, capable of inferring abrupt temporal jumps in the importance of topics. Posterior inference is performed through straightforward Gibbs sampling, based on the forward-filtering backward-sampling algorithm. Moreover, an efficient da...
In recent years social media have become indispensable tools for information dissemination, operatin...
A fundamental problem in text data mining is to extract meaningful structure from document streams ...
Recent work in statistical topic models has investigated richer structures to capture either tempora...
We propose a semi-parametric and dynamic rank factor model for topic model-ing, capable of (i) disco...
The domain of data mining and machine learning has expanded rapidly in recent years to include both ...
Topic modeling is an important area which aims at indexing and exploring massive data streams. In th...
As massive repositories of real-time human commentary, so-cial media platforms have arguably evolved...
We introduce dynamic correlated topic models (DCTM) for analyzing discrete data over time. This mode...
Dynamic topic models (DTM) are commonly used for mining latent topics in evolving web corpora. In th...
We propose a new model for rank aggregation from pairwise comparisons that captures both ranking het...
Information extraction from large corpora can be a useful tool for many applications in industry and...
Learning a dictionary of basis elements with the objective of building compact data representations ...
Topic models and all their variants analyse text by learning meaningful representations through word...
Personalized ranking methods are at the core of many systems that learn to produce recommendations f...
In recent years social media have become indispensable tools for information dissemination, operatin...
In recent years social media have become indispensable tools for information dissemination, operatin...
A fundamental problem in text data mining is to extract meaningful structure from document streams ...
Recent work in statistical topic models has investigated richer structures to capture either tempora...
We propose a semi-parametric and dynamic rank factor model for topic model-ing, capable of (i) disco...
The domain of data mining and machine learning has expanded rapidly in recent years to include both ...
Topic modeling is an important area which aims at indexing and exploring massive data streams. In th...
As massive repositories of real-time human commentary, so-cial media platforms have arguably evolved...
We introduce dynamic correlated topic models (DCTM) for analyzing discrete data over time. This mode...
Dynamic topic models (DTM) are commonly used for mining latent topics in evolving web corpora. In th...
We propose a new model for rank aggregation from pairwise comparisons that captures both ranking het...
Information extraction from large corpora can be a useful tool for many applications in industry and...
Learning a dictionary of basis elements with the objective of building compact data representations ...
Topic models and all their variants analyse text by learning meaningful representations through word...
Personalized ranking methods are at the core of many systems that learn to produce recommendations f...
In recent years social media have become indispensable tools for information dissemination, operatin...
In recent years social media have become indispensable tools for information dissemination, operatin...
A fundamental problem in text data mining is to extract meaningful structure from document streams ...
Recent work in statistical topic models has investigated richer structures to capture either tempora...
Add to ORKG