Real-world classification problems typically exhibit an imbalanced or long-tailed label
distribution, wherein many labels are associated with only a few samples. This poses a …

Learning to hash has been widely applied to approximate nearest neighbor search for large-
scale multimedia retrieval, due to its computation efficiency and retrieval quality. Deep …

We present a unifying framework for information theoretic feature selection, bringing almost
two decades of research on heuristic filter criteria under a single theoretical interpretation …

Due to its computation efficiency and retrieval quality, hashing has been widely applied to
approximate nearest neighbor search for large-scale image retrieval, while deep hashing …

Performance evaluation of supervised classification learning method related to its prediction
ability on independent data is very important in machine learning. It is also almost …

Approximate nearest neighbour search (ANNS) in high-dimensional space is an essential
and fundamental operation in many applications from many domains such as multimedia …

Graph-level anomaly detection aims to distinguish anomalous graphs in a graph dataset
from normal graphs. Anomalous graphs represent a very few but essential patterns in the …

Cross-modal hashing enables similarity retrieval across different content modalities, such as
searching relevant images in response to text queries. It provides with the advantages of …

Predictive models are increasingly being used to optimize decision-making and minimize
costs. A conventional approach is predict-then-optimize: first, a predictive model is built; …

Deep learning to hash improves image retrieval performance by end-to-end representation
learning and hash coding from training data with pairwise similarity information. Subject to …