Multi-label classification is an important machine learning task wherein one assigns a
subset of candidate labels to an object. In this paper, we propose a new multi-label …

Extracting parts of a text document relevant to a class label is a critical information retrieval
task. We propose a semi-supervised multi-label topic model for jointly achieving document …

The Dirichlet process and its extension, the Pitman–Yor process, are stochastic processes
that take probability distributions as a parameter. These processes can be stacked up to …

Recently, supervised topic modeling approaches have received considerable attention.
However, the representative labeled latent Dirichlet allocation (L-LDA) method has a …

Social media offers a new communication channel for users and affords an interactive
opportunity between users and the firms about the products and the brands. Understanding …

Dataless text classification, ie, a new paradigm of weakly supervised learning, refers to the
task of learning with unlabeled documents and a few predefined representative words of …

It has recently been suggested that assuming independence between labels is not suitable
for real-world multi-label classification. To account for label dependencies, this paper …

Supervised topic modeling algorithms have been successfully applied to multi-label
document classification tasks. Representative models include labeled latent Dirichlet …

We present the hierarchical Dirichlet scaling process (HDSP), a Bayesian nonparametric
mixed membership model for multi-labeled data. We construct the HDSP based on the …

Many classification tasks require both labeling objects and determining label associations
for parts of each object. Example applications include labeling segments of images or …