A Novel Adaptive Multi-View Non-Negative Graph Semi-Supervised ELM

Abstract

This paper represents a semi-supervised learning framework, which integrates multi-view learning, extreme learning machine (ELM) and graph-based semi-supervised learning. The aim is to expand the scope of adaptation of non-negative sparse graph (NNSG) framework, under a multi-view condition and a non-linear relationship. The proposed multi-view learning method will be adaptive since when data is single-view the framework will degenerate into an embedded framework for NNSG framework. The proposed ELM method also will be adaptive since the number of hidden layer neuron will change with different number of input and output layer neuron. The combination of both proposed methods outperforms traditional graph-based semisupervised learning, such as flexible manifold embedding (FME) and NNSG framework, which can not establish an affinity matrix for multi-view and can not establish a non-liner model for unknown data. Unlike traditional graph-based semi-supervised learning methods, which only can label propagation and build linear regression models for single or multi-view data, our proposed method has an obvious advantage that is applicable to any single or multi-view data, and builds linear or non-linear models. We provides extensive experiments on four public database in order to evaluate the performance of the proposed method. These experiments demonstrate significant improvement over the state-of-the-art algorithms in label propagation and processing of new data. INDEX TERMS Graph-based semi-supervised learning, Multi-view learning, Semi-supervised ELM, Graph learning.

RETRACTED ARTICLE: Tensor based stacked fuzzy neural network for efficient data regression

“… 2020 ; Zheng et al. 2020 ). By using the regularized correntropy criterion and half-quadratic optimization technique, convergence speed and performance are both showed superiorities than the original (Yang et al.…”

Semi-Supervised Medical Image Classification with Pseudo Labels Using Coalition Similarity Training

“…SSL is a learning strategy to improve the performance of deep neural networks by utilizing unlabeled samples when the amount of labeled data are sparse [13]. Current SSL methods can be categorized into four groups, including generative models [14], consistency regularization [15], graph neural network models (GNNs) [16], and pseudo-labeling [17].…”

A Tensor Based Stacked Fuzzy Networks for Efficient Data Regression

“…Generalization performance is the main concern for the learning algorithms, balancing computational complexity and generalization ability have been extended via ELM (Ragusa et al, 2020); with the designed data and modeled parallel ELMs, large-scale learning tasks could be tackled by ELM (Ming et al, 2018), moreover, a tradeoff should be made among efficiency and scalability, the algorithm should have complementary advantages. With the aid of graph learning and adaptive unsupervised/semi-supervised clustering method, flexible and discriminative data embedding could be achieved (Zeng et al, 2020b;Zheng et al, 2020). By using the regularized correntropy criterion and half-quadratic optimization technique, convergence speed and performance are both showed superiorities than the original (Yang et al, 2020a), and the robust type algorithm has been studied in (Yang et al, 2020b).…”