Single-cell omics technologies have revolutionized the study of gene regulation in complex
tissues. A major computational challenge in analyzing these datasets is to project the large …

We present a new clustering method in the form of a single clustering equation that is able to
directly discover grou**s in the data. The main proposition is that the first neighbor of each …

Spectral clustering refers to a family of well-known unsupervised learning algorithms. Rather
than attempting to cluster points in their native domain, one constructs a (usually sparse) …

Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we
study a version of constrained SC in which we try to incorporate the fairness notion …

Generating a low-rank matrix approximation is very important in large-scale machine
learning applications. The standard Nyström method is one of the state-of-the-art techniques …

In order to solve the problem that the traditional spectral clustering algorithm is time-
consuming and resource consuming when applied to large-scale data, resulting in poor …

Multi-view spectral clustering can effectively reveal the intrinsic clustering structure among
data by performing clustering on the learned optimal embedding across views. Though …

Spectral clustering is a powerful unsupervised machine learning algorithm for clustering
data with nonconvex or nested structures [AY Ng, MI Jordan, and Y. Weiss, On spectral …

The clustering ensemble technique aims to combine multiple clusterings into a probably
better and more robust clustering and has been receiving an increasing attention in recent …

Multi-view spectral clustering aims to group data into different categories by optimally
exploring complementary information from multiple Laplacian matrices. However, existing …