With the ability to exploit the internal structure of data, graph-based models have received a
lot of attention and have achieved great success in multiview subspace clustering for …

Spectral clustering (SC) has become one of the most widely-adopted clustering algorithms,
and been successfully applied into various applications. We in this work explore the problem …

Spectral clustering has been demonstrated to often outperform K-means clustering in real
applications because it improves the similarity measurement of K-means clustering …

Feature self-representation has become the backbone of unsupervised feature selection,
since it is almost insensitive to noise data. However, feature selection methods based on …

Spectral Clustering (SC) has been the main subject of intensive research due to its
remarkable clustering performance. Despite its successes, most existing SC methods suffer …

In the past decades, spectral clustering (SC) has become one of the most effective clustering
algorithms. However, most previous studies focus on spectral clustering tasks with a fixed …

Clustering, as an effective data analysis technique, is widely used in industrial application
and science research. In this paper, we have proposed a novel spectral clustering method to …

Spectral clustering, one of the most popular clustering methods, has attracted considerable
attention in many fields owing to its excellent empirical properties. However, previously …

Aiming at traditional spectral clustering method still suffers from the following issues: 1)
unable to handle the incomplete data, 2) two-step clustering strategies tend to perform …

Spectral clustering makes use of the spectrum of an input affinity matrix to segment data into
disjoint clusters. The performance of spectral clustering depends heavily on the quality of the …