Adaptive multiple graph regularized semi-supervised extreme learning machine

Yi, Yugen; Qiao, Shaojie; Zhou, Wei; Zheng, Caixia; Liu, Qinghua; Wang, Jianzhong

doi:10.1007/s00500-018-3109-x

Cited by 25 publications

(9 citation statements)

References 55 publications

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“…In recent years, how to use local consistency of data for learning to improve the performance of machine learning methods that has attracted researchers' attention [45]. Based on the theory that similar samples should have similar properties, the graph regularization is combined with our method to preserve the local structural information, which may improve the classification performance of the method [13,47]. We use the label information of the training sample to construct an adjacent graph, and the regularization term of the graph is integrated to constrain the output weight matrix, so as to learn the similar output of similar samples.…”

Section: Proposed Csrgelmmentioning

confidence: 99%

Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

et al. 2020

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Background As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM. Results In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

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Section: Proposed Csrgelmmentioning

confidence: 99%

Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

et al. 2020

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“…After feature extraction, the objective of the next step is to assign each pixel to one of two categories: shadow and object. The ELM [43][44][45][46][47][48] is adopted for classification, which has been proven to be effective and efficient in addressing a variety of classification problems. Next, the classification procedure using the ELM algorithm is described in detail.…”

Section: Classification Using Extreme Learning Machinementioning

confidence: 99%

“…As a special type of single-hidden layer feed-forward neural network, an extreme learning machine (ELM) has been extensively applied in several fields of machine learning [43][44][45][46][47][48], in which the parameters are randomly generated and the values of parameters do not need to be tuned. Ghimire and Lee [48] proposed an online sequential extreme learning machine-based semi-supervised technique for moving cast shadow detection, which provided better generalization performance.…”

Section: Introductionmentioning

confidence: 99%

An Effective Framework Using Spatial Correlation and Extreme Learning Machine for Moving Cast Shadow Detection

Dai

Wang

et al. 2019

Applied Sciences

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Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, we present a novel moving cast-shadow detection framework based on the extreme learning machine (ELM) to efficiently distinguish shadow points from the foreground object. First, according to the physical model of shadows, pixel-level features of different channels in different color spaces and region-level features derived from the spatial correlation of neighboring pixels are extracted from the foreground. Second, an ELM-based classification model is developed by labelled shadow and un-shadow points, which is able to rapidly distinguish the points in the new input whether they belong to shadows or not. Finally, to guarantee the integrity of shadows and objects for further image processing, a simple post-processing procedure is designed to refine the results, which also drastically improves the accuracy of moving shadow detection. Extensive experiments on two publicly common datasets including 13 different scenes demonstrate that the performance of the proposed framework is superior to representative state-of-the-art methods.

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“…For example, the largest value of f ij means the highest probability of the sample x i belonging to j-th class. In terms of the LP theory [37,38], the nearby or similar samples and the samples from the same global cluster should share similar labels. Therefore, the objective function of LP is defined as follows:…”

Section: Objective Function Of Ssrr-aglpmentioning

confidence: 99%

“…Hence, it is difficult to satisfy the requirement of for real-life sample [37]. Conversely, the unlabeled samples can easily be collected from the Internet, web chatting, digital camera of surveillance and so on [38]. As a result, it is necessary to utilize the information of unlabeled samples to improve the performance of ridge regression algorithm [39,40].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Ridge Regression with Adaptive Graph-Based Label Propagation

Chen

Dai

et al. 2018

Applied Sciences

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In order to overcome the drawbacks of the ridge regression and label propagation algorithms, we propose a new semi-supervised classification method named semi-supervised ridge regression with adaptive graph-based label propagation (SSRR-AGLP). Firstly, we present a new adaptive graph-learning scheme and integrate it into the procedure of label propagation, in which the locality and sparsity of samples are considered simultaneously. Then, we introduce the ridge regression algorithm into label propagation to solve the "out of sample" problem. As a consequence, the proposed SSSRR-AGLP integrates adaptive graph learning, label propagation and ridge regression into a unified framework. Finally, an effective iterative updating algorithm is designed for solving the algorithm, and the convergence analysis is also provided. Extensive experiments are conducted on five databases. Through comparing the results with some well-known algorithms, the effectiveness and superiority of the proposed algorithm are demonstrated.

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Adaptive multiple graph regularized semi-supervised extreme learning machine

Cited by 25 publications

References 55 publications

Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

An Effective Framework Using Spatial Correlation and Extreme Learning Machine for Moving Cast Shadow Detection

Semi-Supervised Ridge Regression with Adaptive Graph-Based Label Propagation

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