Robust kernelized graph-based learning

Supratim Manna

doi:10.1016/j.patcog.2020.107628

Return

Robust kernelized graph-based learning

2021-02-01

PRE

Supratim Manna *

Jessy Rimaya Khonglah

Anirban Mukherjee

Goutam Saha

DOI：10.1016/j.patcog.2020.107628

Original

Original request for help

Save

Abstract

En 中文

The studies of hidden complex structures in data have popularized the use of graph-based learning meth-ods in semi-supervised and unsupervised learning tasks. Kernelized graph-based methods are proven to perform better, but these methods suffer from the issue of appropriate kernel selection. Instead of using multiple views, these methods generally use a single view. But multi-view methods need a proper weight assignment technique to each view in proportion to their contribution to the learning task. To solve this, a novel Self-weighted Multi-view Multiple Kernel Learning (SMVMKL) framework is proposed using multiple kernels on multiple views that automatically assigns appropriate weight to each kernel of each view without introducing an additional parameter. But the real-world data that is either noisy or corrupt with outliers which may effect the performance of the proposed SMVMKL method. To deal with this, a Robust Self-weighted Multi-view Multiple Kernel Learning (RSMVMKL) framework using the l(2,1)-norm has also been proposed that reduces the effect of outliers present in the data set. Both the proposed methods have been evaluated on multiple benchmark data sets and result in a performance comparable with the other state-of-the-art multi-view methods considered in this paper. (c) 2020 Elsevier Ltd. All rights reserved.

Keywords:

Robust

Clustering

Semi-supervised classification

Multiple kernels

Multiple views

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.