Face recognition has witnessed great progresses in recent years, mainly attributed to the
high-capacity model designed and the abundant labeled data collected. However, it …

The research in automatic unsupervised visual clustering has received considerable
attention over the last couple years. It aims at explaining distributions of unlabeled visual …

Accurate identification of the cancer types is essential to cancer diagnoses and treatments.
Since cancer tissue and normal tissue have different gene expression, gene expression …

The advances over the past several years have pushed the performance of face recognition
to an amazing level. This great success, to a large extent, is built on top of millions of …

Face recognition algorithms are generally trained for matching high-resolution images and
they perform well for similar resolution test data. However, the performance of such systems …

Semi-supervised learning, which uses a large amount of unlabeled data to improve the
performance of a classifier when only a limited amount of labeled data is available, has …

Analysing the face recognition rate of various current face recognition algorithms is
absolutely critical in developing new robust algorithms. In his paper we report performance …

Deep facial recognition benefits significantly from large-scale training data; however, the
bottleneck of high labeling costs persists. Therefore, to reduce the labeling costs, it is …

High-dimensional non-Gaussian data is widely found in the real world, such as in face
recognition, facial expressions, document recognition, and text processing. Linear …

In real-world applications, different kinds of learning and prediction errors are likely to incur
different costs for the same system. Moreover, in practice, the cost label information is often …