Comparison of Latent Dirichlet Modeling and Factor Analysis for Topic Extraction: A Lesson of History

Topic modeling is often perceived as a relatively new development in information retrieval sciences, and new methods such as Probabilistic Latent Semantic Analysis and Latent Dirichlet Allocation have generated a lot of research. However, attempts to extract topics from unstructured text using Factor Analysis techniques can be found as early as the 1960s. This paper compares the perceived coherence of topics extracted on three different datasets using Factor Analysis and Latent Dirichlet Allocation. To perform such a comparison a new extrinsic evaluation method is proposed. Results suggest that Factor Analysis can produce topics perceived by human coders as more coherent than Latent Dirichlet Allocation and warrant a revisit of a topic extraction method developed more than fifty-five years ago, yet forgotten

Learning Multimodal Latent Attributes

Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning