Probabilistic topic models for human emotion analysis

While discrete emotions like joy, anger, disgust etc. are quite popular, continuous

emotion dimensions like arousal and valence are gaining popularity within the research

community due to an increase in the availability of datasets annotated with these

emotions. Unlike the discrete emotions, continuous emotions allow modeling of subtle

and complex affect dimensions but are difficult to predict.

Dimension reduction techniques form the core of emotion recognition systems and

help create a new feature space that is more helpful in predicting emotions. But these

techniques do not necessarily guarantee a better predictive capability as most of them

are unsupervised, especially in regression learning. In emotion recognition literature,

supervised dimension reduction techniques have not been explored much and in this

work a solution is provided through probabilistic topic models. Topic models provide

a strong probabilistic framework to embed new learning paradigms and modalities.

In this thesis, the graphical structure of Latent Dirichlet Allocation has been explored

and new models tuned to emotion recognition and change detection have been built.

In this work, it has been shown that the double mixture structure of topic models

helps 1) to visualize feature patterns, and 2) to project features onto a topic simplex

that is more predictive of human emotions, when compared to popular techniques

like PCA and KernelPCA. Traditionally, topic models have been used on quantized

features but in this work, a continuous topic model called the Dirichlet Gaussian

Mixture model has been proposed. Evaluation of DGMM has shown that while modeling

videos, performance of LDA models can be replicated even without quantizing

the features. Until now, topic models have not been explored in a supervised context

of video analysis and thus a Regularized supervised topic model (RSLDA) that

models video and audio features is introduced. RSLDA learning algorithm performs

both dimension reduction and regularized linear regression simultaneously, and has outperformed supervised dimension reduction techniques like SPCA and Correlation

based feature selection algorithms. In a first of its kind, two new topic models, Adaptive

temporal topic model (ATTM) and SLDA for change detection (SLDACD) have

been developed for predicting concept drift in time series data. These models do not

assume independence of consecutive frames and outperform traditional topic models

in detecting local and global changes respectively.