A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

Although existing semi-supervised learning models achieve remarkable success in learning
with unannotated in-distribution data, they mostly fail to learn on unlabeled data sampled …

k nearest neighbor (kNN) method is a popular classification method in data mining and
statistics because of its simple implementation and significant classification performance …

Spectral clustering (SC) has been widely applied to various computer vision tasks, where
the key is to construct a robust affinity matrix for data partitioning. With the increase in visual …

Spectral clustering (SC) is one of the most widely used methods for data clustering. It first
finds a low-dimensional embedding U of data by computing the eigenvectors of the …

Spectral clustering has found extensive use in many areas. Most traditional spectral
clustering algorithms work in three separate steps: similarity graph construction; continuous …

Spectral clustering requires robust and meaningful affinity graphs as input in order to form
clusters with desired structures that can well support human intuition. To construct such …

Subspace clustering refers to the problem of finding low-dimensional subspaces (clusters)
for high-dimensional data. Current state-of-the-art subspace clustering methods are usually …

Measuring the similarity between two instances reliably, shape or image, is a challenging
problem in shape and image retrieval. In this paper, a simple yet effective method called …

This work presents a suprathreshold fiber cluster (STFC) method that leverages the whole
brain fiber geometry to enhance statistical group difference analyses. The proposed method …