Multi-view unsupervised feature selection by cross-diffused matrix alignment

Wei, Xiaokai; Cao, Binggang; Yu, Philip S.

doi:10.1109/ijcnn.2017.7965894

Cited by 17 publications

(3 citation statements)

References 18 publications

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“…Voting has been proven to be effective in many classification or clustering applications. For example, cumulative voting was proposed to solve the problem of cluster label alignment [16,21]. Accuracy can be improved in compared to using individual clustering algorithms.…”

Section: Edge Detection Ensemble Via Local Detectors' Votingmentioning

confidence: 99%

MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact

Chang¹

2018

AJCST

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Magnetic resonance imaging (MRI) has revolutionized radiology in past four decades. MR image edge detection can identify anatomy boundaries and extract features for image analysis applications like segmentation and recognition of anatomy structures. Traditional MR image edge detection methods directly identify discontinuities in MR image domain without considering distribution of noise and aliasing artifact produced from MR scanner and reconstruction. It is difficult to suppress effects of noise and aliasing artifact during the edge detection process. In this project, a novel MR brain image edge detection method is proposed, which is based on parallel MRI reconstruction method. Distribution of noise and aliasing artifact is characterized by geometry factor map that also guides edge detection process for avoiding detection of noise and aliasing artifact. A collaborative learning strategy is applied on voting edges for producing the final edge detection. Experimental results show that the proposed method not only keep anatomy structure boundaries without missing edge components, but also avoid detection of noise and artifact with wrong edges.

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Section: Edge Detection Ensemble Via Local Detectors' Votingmentioning

confidence: 99%

MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact

Chang¹

2018

AJCST

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“…Others have also used feature selection in different areas [7], [25], [26], [27]. By finding important or significant patterns, feature selection can improve the overall performance of a system.…”

Section: Related Workmentioning

confidence: 99%

Contaminant removal for Android malware detection systems

Sun

Wei

Zhang

et al. 2017

2017 IEEE International Conference on Big Data (Big Data)

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A recent report indicates that there is a new malicious app introduced every 4 seconds. This rapid malware distribution rate causes existing malware detection systems to fall far behind, allowing malicious apps to escape vetting efforts and be distributed by even legitimate app stores. When trusted downloading sites distribute malware, several negative consequences ensue. First, the popularity of these sites would allow such malicious apps to quickly and widely infect devices. Second, analysts and researchers who rely on machine learning based detection techniques may also download these apps and mistakenly label them as benign since they have not been disclosed as malware. These apps are then used as part of their benign dataset during model training and testing. The presence of contaminants in benign dataset can compromise the effectiveness and accuracy of their detection and classification techniques.To address this issue, we introduce PUDROID (Positive and Unlabeled learning-based malware detection for Android) to automatically and effectively remove contaminants from training datasets, allowing machine learning based malware classifiers and detectors to be more effective and accurate. To further improve the performance of such detectors, we apply a feature selection strategy to select pertinent features from a variety of features. We then compare the detection rates and accuracy of detection systems using two datasets; one using PUDROID to remove contaminants and the other without removing contaminants. The results indicate that once we remove contaminants from the datasets, we can significantly improve both malware detection rate and detection accuracy.

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“…Then, to determine the best intrinsic manifold, they built a multiple graph ensemble regularization framework. Cross diffused matrix alignment based on feature selection is a technique that Wei et al [8] proposed for choosing features for each view while doing alignment on a cross diffused matrix. The final clustering results were then obtained via co-regularized spectral clustering on these chosen features [9].…”

Section: Introductionmentioning

confidence: 99%

Multi-modal Multi-view Clustering based on Non-negative Matrix Factorization

Khalafaoui

Grozavu

Mateï

et al. 2022

2022 IEEE Symposium Series on Computational Intelligence (SSCI)

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By combining related objects, unsupervised machine learning techniques aim to reveal the underlying patterns in a data set.Non-negative Matrix Factorization (NMF) is a data mining technique that splits data matrices by imposing restrictions on the elements' non-negativity into two matrices: one representing the data partitions and the other to represent the cluster prototypes of the data set. This method has attracted a lot of attention and is used in a wide range of applications, including text mining, clustering, language modeling, music transcription, and neuroscience (gene separation). The interpretation of the generated matrices is made simpler by the absence of negative values. In this article, we propose a study on multi-modal clustering algorithms and present a novel method called multi-modal multi-view non-negative matrix factorization, in which we analyze the collaboration of several local NMF models. The experimental results show the value of the proposed approach, which was evaluated using a variety of data sets, and the obtained results are very promising compared to state of art methods.

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Multi-view unsupervised feature selection by cross-diffused matrix alignment

Cited by 17 publications

References 18 publications

MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact

MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact

Contaminant removal for Android malware detection systems

Multi-modal Multi-view Clustering based on Non-negative Matrix Factorization

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