Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …

K-means is a fundamental clustering algorithm widely used in both academic and industrial
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …

Clustering is central to many data-driven application domains and has been studied
extensively in terms of distance functions and grouping algorithms. Relatively little work has …

Clustering is among the most fundamental tasks in computer vision and machine learning. In
this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised …

In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed
ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace …

The goal of this paper is to discover a set of discriminative patches which can serve as a fully
unsupervised mid-level visual representation. The desired patches need to satisfy two …

Unsupervised feature learning has made great strides with contrastive learning based on
instance discrimination and invariant mapping, as benchmarked on curated class-balanced …

In this paper, we propose a new image clustering algorithm, referred to as clustering using
local discriminant models and global integration (LDMGI). To deal with the data points …

Combining information from various data sources has become an important research topic
in machine learning with many scientific applications. Most previous studies employ kernels …

Spectral clustering has been widely used in various aspects, especially the machine
learning fields. Clustering with similarity matrix and low-dimensional representation of data …