This paper aims at constructing a good graph to discover the intrinsic data structures under a
semisupervised learning setting. First, we propose to build a nonnegative low-rank and …

Researchers using social media data want to understand the discussions occurring in and
about their respective fields. These domain experts often turn to topic models to help them …

The goodness of the features extracted from the instances and the number of training
instances are two key components in machine learning, and building an effective model is …

The traditional color harmony models for the photo esthetics assessment, such as Moon &
Spencer׳ s model and the adaptive hue template based approach, only utilize the …

Graph plays a very important role in graph based semi-supervised learning (SSL) methods.
However, most current graph construction methods emphasize on local properties of the …

Topic models, such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet
allocation (LDA), have shown impressive success in many fields. Recently, multi-view …

Image classification is a core task in many applications of computer vision, including object
detection and recognition. It aims at analysing the visual content and automatically …

Latent topic models are applied to analyze the low-dimensional semantic meaning of
documents and images, which are widely used in object categorization. However, the …

Supervised topic models leverage label information to learn discriminative latent topic
representations. As collecting a fully labeled dataset is often time-consuming, semi …

Most of the existing methods for measuring the inter-concept distance (ICD) between two
concepts from their image instances use only a single kind of visual feature extracted from …