Graph construction plays an essential role in graph-based label propagation since graphs
give some information on the structure of the data manifold. While most graph construction …

In spectral clustering (SC), the clustering result highly depends on the similarity graph
matrix. The k-nearest neighbors graph is a popular method to build the similarity graph …

The traditional manifold learning methods usually utilize the original observed data to
directly define the intrinsic structure among data. Because the original samples often contain …

Graph carries out a key role in graph-based semi-supervised label propagation, as it
clarifies the structure of the data manifold. The performance of label propagation methods …

Graph-based embedding methods are very useful for reducing the dimension of high-
dimensional data and for extracting their relevant features. In this paper, we introduce a …

Assessing beauty using facial images analysis is an emerging computer vision problem. To
the best of our knowledge, all existing methods for automatic facial beauty scoring rely on …

Sparse representation-based classifier (SRC) and collaborative representation-based
classifier (CRC) are two commonly used classifiers. There has been pointed out that the …

Label propagation is an important semi-supervised learning method that generalizes the
attributes of labelled samples to unlabelled samples based on the correlation of the data …

Graph construction is a known method of transferring the problem of classic vector data
mining to network analysis. The advantage of networks is that the data are extended by links …

In many pattern recognition applications feature selection and instance selection can be
used as two data preprocessing methods that aim at reducing the computational cost of the …