Graph-based fraud detection approaches have escalated lots of attention recently due to the
abundant relational information of graph-structured data, which may be beneficial for the …

Real-world data often exhibits long-tailed distributions with heavy class imbalance, posing
great challenges for deep recognition models. We identify a persisting dilemma on the value …

With the rapid increase of large-scale, real-world datasets, it becomes critical to address the
problem of long-tailed data distribution (ie, a few classes account for most of the data, while …

Object frequency in the real world often follows a power law, leading to a mismatch between
datasets with long-tailed class distributions seen by a machine learning model and our …

Subspace clustering is an important unsupervised clustering approach. It is based on the
assumption that the high-dimensional data points are approximately distributed around …

Subspace clustering methods based on data self-expression have become very popular for
learning from data that lie in a union of low-dimensional linear subspaces. However, the …

Oil spill detection has attracted increasing attention in recent years, since marine oil spill
accidents severely affect environments, natural resources, and the lives of coastal …

State-of-the-art subspace clustering methods are based on the self-expressive model, which
represents each data point as a linear combination of other data points. However, such …

The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …

Deep subspace clustering (DSC) has achieved remarkable performances in the
unsupervised classification of hyperspectral images. However, previous models based on …