Data granulation emerged as an important paradigm in modeling and computing with
uncertainty, exploiting information granules as the main mathematical constructs involved in …

Support vector machine (SVM), as a supervised learning method, has different kinds of
varieties with significant performance. In recent years, more research focused on nonparallel …

Classical spectral clustering is based on a spectral decomposition of a graph Laplacian,
obtained from a graph adjacency matrix representing positive graph edge weights …

Graphs are commonly used to represent objects, such as images and text, for pattern
classification. In a dynamic world, an object may continuously evolve over time, and so does …

This paper presents a new facial landmark detection method for images and videos under
uncontrolled conditions, based on a proposed Face Alignment Recurrent Network (FARN) …

We argue that the standard graph Laplacian is preferable for spectral partitioning of signed
graphs compared to the signed Laplacian. Simple examples demonstrate that partitioning …

In graph motivated learning, label propagation largely depends on data affinity represented
as edges between connected data points. The affinity assignment implicitly assumes even …

In this work, we address the problem of improving the classification performance of machine
learning models, especially in the presence of noisy and outlier data. To this end, we first …

In [D01: 10.1109/ICMEW. 2014.6890711], a graph-based denoising is performed by
projecting the noisy image to a lower dimensional Krylov subspace of the graph Laplacian …

Graph kernels are of vital importance in the field of graph comparison and classification.
However, how to compare and evaluate graph kernels and how to choose an optimal kernel …