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Robust multi-kernel Nearest Neighborhood for Outlier Detection

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 4220-4231页

作者： Wang, Xinye Duan, Lei Yu, Zhenyang He, Chengxin Bao, Zhifeng Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China RMIT Univ Melbourne Vic 3000 Australia

Outlier detection methods based on distance measure have been used in numerous applications due to their effectiveness and interpretability. However, distances among instances heavily depend on the feature space in which they reside. For an outlier, distances from it to the normal instances may be extremely close in one feature space, failing to separate them from each other, while this situation is reversed in another space. Meanwhile, the distance measure is sensitive to a few "marginal instances" (i.e., normal instances located very close to outliers in the feature space) during the estimation of whether a test instance is an outlier or not. In this article, we propose a robust multi-kernel nearest neighborhood (RMKN) method for outlier detection. Specifically, in the training phase, we only consider normal instances and transform them into a Polynomial kernel function weighted digraph to capture their geometric relationships in the original feature space. Then, we develop an objective function based on the weighted digraph to find a latent feature space via multi-kernel learning such that distances among normal instances in this latent feature space are as close as possible while preserving their original distributions. In the detecting phase, we design an outlying score based on the two-stage multi-kernel $k$k-nearest nearest neighbors to detect outliers. Extensive experiments with ten datasets show that RMKN is effective and robust.

关键词： Anomaly detection kernel Training Games Feature extraction Task analysis Support vector machines Outlier detection multi-kernel learning weighted digraph nearest neighborhood

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multi-omics fusion with soft labeling for enhanced prediction of distant metastasis in nasopharyngeal carcinoma patients after radiotherapy

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COMPUTERS IN BIOLOGY AND MEDICINE 2024年 168卷 107684-107684页

作者： Sheng, Jiabao Lam, SaiKit Zhang, Jiang Zhang, Yuanpeng Cai, Jing Hong Kong Polytech Univ Dept Hlth Technol & Informat Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Ageing Hong Kong Peoples R China Hong Kong Polytech Univ Dept Biomed Engn Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen Peoples R China Hong Kong Polytech Univ Dept Hlth Technol & Informat Hung Hom 11 Yuk Choi Rd Hong Kong Peoples R China

Omics fusion has emerged as a crucial preprocessing approach in medical image processing, significantly assisting several studies. One of the challenges encountered in integrating omics data is the unpredictability arising from disparities in data sources and medical imaging equipment. Due to these differences, the distribution of omics futures exhibits spatial heterogeneity, diminishing their capacity to enhance subsequent tasks. To overcome this challenge and facilitate the integration of their joint application to specific medical objectives, this study aims to develop a fusion methodology for nasopharyngeal carcinoma (NPC) distant metastasis prediction to mitigate the disparities inherent in omics data. The multi-kernel late-fusion method can reduce the impact of these differences by mapping the features using the most suiTable single-kernel function and then combining them in a high-dimensional space that can effectively represent the data. The proposed approach in this study employs a distinctive framework incorporating a label-softening technique alongside a multi-kernel-based Radial basis function (RBF) neural network to address these limitations. An efficient representation of the data may be achieved by utilizing the multi-kernel to map the inherent features and then merging them in a space with many dimensions. However, the inflexibility of label fitting poses a constraint on using multi-kernel late-fusion methods in complex NPC datasets, hence affecting the efficacy of general classifiers in dealing with high-dimensional characteristics. The label softening increases the disparity between the two cohorts, providing a more flexible structure for allocating labels. The proposed model is evaluated on multi-omics datasets, and the results demonstrate its strength and effectiveness in predicting distant metastasis of NPC patients.

关键词： multi-omics fusion multi-kernel learning multi-modality

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learning protein subcellular localization multi-view patterns from heterogeneous data of imaging, sequence and networks

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BRIEFINGS IN BIOINFORMATICS 2022年第2期23卷 bbab539-bbab539页

作者： Wang, Ge Xue, Min-Qi Shen, Hong-Bin Xu, Ying-Ying Southern Med Univ Sch Biomed Engn Guangzhou 510515 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Southern Med Univ Guangzhou Guangdong Peoples R China

Location proteomics seeks to provide automated high-resolution descriptions of protein location patterns within cells. Many efforts have been undertaken in location proteomics over the past decades, thereby producing plenty of automated predictors for protein subcellular localization. However, most of these predictors are trained solely from high-throughput microscopic images or protein amino acid sequences alone. Unifying heterogeneous protein data sources has yet to be exploited. In this paper, we present a pipeline called sequence, image, network-based protein subcellular locator (SIN-Locator) that constructs a multi-view description of proteins by integrating multiple data types including images of protein expression in cells or tissues, amino acid sequences and protein-protein interaction networks, to classify the patterns of protein subcellular locations. Proteins were encoded by both handcrafted features and deep learning features, and multiple combining methods were implemented. Our experimental results indicated that optimal integrations can considerately enhance the classification accuracy, and the utility of SIN-Locator has been demonstrated through applying to new released proteins in the human protein atlas. Furthermore, we also investigate the contribution of different data sources and influence of partial absence of data. This work is anticipated to provide clues for reconciliation and combination of multi-source data for protein location analysis.

关键词： protein subcellular location multi-source data deep learning multi-kernel learning

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kernelized multi-granulation fuzzy rough set over hybrid attribute decision system and application to stroke risk prediction

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APPLIED INTELLIGENCE 2023年第21期53卷 24876-24894页

作者： Wang, Ting Sun, Bingzhen Jiang, Chao Xidian Univ Sch Econ & Management Xian Shaanxi Peoples R China Xian Med Univ Affiliated Hosp 2 Xian Shaanxi Peoples R China Shaanxi Key Lab Brain Disorders Xian Shaanxi Peoples R China

Searching for quantitative scientific prediction models and methods under uncertain hybrid information environment is helpful to the scientific decision-making of practical management problems. In this paper, we discuss the uncertain decision making problem with hybrid attributes and fuzzy decision objects. Considering the characteristic of hybrid attribute information, we introduce kernel functions to abstract the similarities from different types of attributes. On this basis, the kernel-based upper and lower approximations of arbitrary fuzzy decision objects over hybrid attribute information system are given under a multi-granularity framework, i.e., kernelized multi-granulation fuzzy rough sets, called KMGFRS in this paper. Then, the attribute reduction on hybrid attribute decision system based on KMGFRS is discussed. In addition, we combine KMGFRS and fuzzy k-nearest neighbors (FkNN) to propose a new prediction method without prior information and apply it to stroke risk assessment and prediction in clinical decision-making. Finally, we use real clinical data and UCI datasets for experiment analysis, and the results verify the applicability and validity of the model.

关键词： Rough set multi-granulation fuzzy rough set multi-kernel learning Fuzzy kNN Stroke risk prediction

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MKMR: a multi-kernel machine regression model to predict health outcomes using human microbiome data

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BRIEFINGS IN BIOINFORMATICS 2023年第3期24卷 bbad158-bbad158页

作者： Li, Bing Wang, Tian Qian, Min Wang, Shuang Columbia Univ Mailman Sch Publ Hlth Dept Biostat 722 West 168th St New York NY 10032 USA Brown Univ Sch Publ Hlth Providence RI USA Columbia Univ New York NY 10032 USA

Studies have found that human microbiome is associated with and predictive of human health and diseases. Many statistical methods developed for microbiome data focus on different distance metrics that can capture various information in microbiomes. Prediction models were also developed for microbiome data, including deep learning methods with convolutional neural networks that consider both taxa abundance profiles and taxonomic relationships among microbial taxa from a phylogenetic tree. Studies have also suggested that a health outcome could associate with multiple forms of microbiome profiles. In addition to the abundance of some taxa that are associated with a health outcome, the presence/absence of some taxa is also associated with and predictive of the same health outcome. Moreover, associated taxa may be close to each other on a phylogenetic tree or spread apart on a phylogenetic tree. No prediction models currently exist that use multiple forms of microbiome-outcome associations. To address this, we propose a multi-kernel machine regression (MKMR) method that is able to capture various types of microbiome signals when doing predictions. MKMR utilizes multiple forms of microbiome signals through multiple kernels being transformed from multiple distance metrics for microbiomes and learn an optimal conic combination of these kernels, with kernel weights helping us understand contributions of individual microbiome signal types. Simulation studies suggest a much-improved prediction performance over competing methods with mixture of microbiome signals. Real data applicants to predict multiple health outcomes using throat and gut microbiome data also suggest a better prediction of MKMR than that of competing methods.

关键词： microbiome distances metrics prediction model multi-kernel learning

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Self-Weighted multi-kernel multi-Label learning for Potential miRNA-Disease Association Prediction

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MOLECULAR THERAPY-NUCLEIC ACIDS 2019年 17卷 414-423页

作者： Pan, Zhenxia Zhang, Huaxiang Liang, Cheng Li, Guanghui Xiao, Qiu Ding, Pingjian Luo, Jiawei Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Shandong Peoples R China East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha 410006 Hunan Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China

Researchers have realized that microRNAs (miRNAs) play significant roles in the pathogenesis of various diseases. Although many computational models have been proposed to predict the associations between miRNAs and diseases, prediction performance could still be improved. In this paper, we propose a novel self-weighted, multi-kernel, multi-label learning (SwMKML) method to predict disease-related miRNAs. SwMKML adaptively learns two optimal kernel matrices for both miRNAs and diseases from multiple kernels constructed from known miRNA-disease associations. Moreover, the miRNA-disease associations predicted from both spaces are updated simultaneously based on a multi-label framework. Compared with four state-of-the-art computational models, SwMKML achieved best results of 95.5%, 93.1%, and 84.1% in global leave-one-out cross-validation, 5-fold cross-validation, and overall prediction accuracy, respectively. A case study conducted on head and neck neoplasms further identified two potential prognostic biomarkers, hsa-mir-125b-1 and hsa-mir-125b-2, for the disease. SwMKML is freely available at Github, and we anticipate that it may become an effective tool for potential miRNA-disease association prediction.

关键词： graph-based learning miRNA-disease association prediction multi-kernel learning

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kernel alignment-based three-way clustering on attribute space and its application in stroke risk identification

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INTERNATIONAL JOURNAL OF MACHINE learning AND CYBERNETICS 2022年第6期13卷 1697-1711页

作者： Wang, Ting Sun, Bingzhen Jiang, Chao Weng, Heng Chu, Xiaoli Xidian Univ Sch Econ & Management Xian Shaanxi Peoples R China Xian Med Univ Dept Neurol 3 Affiliated Hosp 2 Xian Shaanxi Peoples R China Shaanxi Key Lab Brain Disorders Xian Shaanxi Peoples R China Guangzhou Univ Chinese Med State Key Lab Dampness Syndrome Chinese Med Affiliated Hosp 2 Guangzhou 510120 Peoples R China

Identifying the key risk factors of disease from a large amount of clinical data is a prerequisite for further scientific decision-making. In medical practice, the clinical symptom information of patients usually includes various types of data. Meanwhile, the occurrence and development of diseases are joint result of the mutual influence factors. Therefore, there is usually a correlation between attributes. In this paper, we discuss a kind of hybrid attribute feature selection problem considering the correlation between attributes. Firstly, we take the identification of disease pathogenic factors in medical decision as the background, and construct a hybrid attribute decision system. Secondly, by introducing kernel alignment, the uncertain relationship between attributes is defined. Based on this, a three-way clustering model in attribute space is established. Furthermore, a feature selection method for hybrid attribute data based on three-way clustering in attribute space is proposed. Finally, we applied the proposed model to identify the pathogenic factors of stroke and used 279 clinical random samples for simulation analysis. The results verified the applicability and validity of the model. The main contributions of this paper include two aspects. In terms of theory, by introducing kernel alignment, a three-way clustering algorithm in attribute space is established. Meanwhile, a hybrid attribute feature selection method based on three-way clustering is proposed. In terms of application, the proposed method is applied to identify risk factors of stroke.

关键词： Three-way clustering kernel alignment multi-kernel learning Feature selection

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multi-kernel graph fusion for spectral clustering

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INFORMATION PROCESSING & MANAGEMENT 2022年第5期59卷

作者： Zhou, Bo Liu, Wenliang Zhang, Wenzhen Lu, Zhengyu Tan, Qianlin Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Guangxi Peoples R China Hechi Univ Hechi 546300 Guangxi Peoples R China

Many methods of multi-kernel clustering have a bias to power base kernels by ignoring other kernels. To address this issue, in this paper, we propose a new method of multi-kernel graph fusion based on min-max optimization (namely MKGF-MM) for spectral clustering by making full use of all base kernels. Specifically, the proposed method investigates a novel min-max weight strategy to capture the complementary information among all base kernels. As a result, every base kernel contributes to the construction of the fusion graph from all base kernels so that the quality of the fusion graph is guaranteed. In addition, we design an iterative optimization method to solve the proposed objective function. Furthermore, we theoretically prove that our optimization method achieves convergence. Experimental results on real medical datasets and scientific datasets demonstrate that the proposed method outperforms all comparison methods and the proposed optimization method achieves fast convergence.

关键词： multi-kernel learning Affinity graph Self-expression Spectral clustering

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multi-omics Cancer Subtype Recognition Based on multi-kernel Partition Aligned Subspace Clustering 1

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Liu, Jian Hou, Long Ge, Shuguang China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China

ISBN: (数字)9789819947492

ISBN: (纸本)9789819947485;9789819947492

With the widespread application of high-throughput technologies, multi-omics data are playing an increasingly important role in cancer subtyping. However, the heterogeneity and high-dimensionality of different omics data make it a challenging task to integrate them into a consistent model. In this paper, we propose a novel multi-omics cancer subtyping method based on multi-kernel Partition Alignment Subspace clustering (MKPAS). Given multiple omics datasets, MKPAS first uses multiple kernel functions to generate kernel matrices as the input of multi-view subspace learning model. Second, it uses subspace learning and rank constraint for each omics data learning an ideal graph structure. Third, to make the clustering results consistent from all omics datasets, a common clustering indication matrix is learned by using the multi-view partition alignment method. Finally, MKPAS integrates multi-kernel learning, subspace learning, graph learning, weights correction and partition alignment into a unified framework, making the process of multi-omics data clustering more intuitive and the clustering results more reliable. To validate the effectiveness of MKPAS, we conduct cancer subtyping experiments on multiple TCGA datasets. The experimental results show that MKPAS is effective for cancer subtype prediction.

关键词： Cancer subtype recognition multi-kernel learning multi-view subspace clustering Partition alignment

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House Price Prediction via Visual Cues and Estate Attributes 17th

House Price Prediction via Visual Cues and Estate Attributes

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17th International Symposium on Visual Computing (ISVC)

作者： Vaddi, Sai Surya Yousif, Amira Baraheem, Samah Shen, Ju Nguyen, Tam, V Univ Dayton Dept Comp Sci Dayton OH 45469 USA

ISBN: (纸本)9783031207150;9783031207167

The price of a house depends on many factors, such as its size, location, amenities, surrounding establishments, and the season in which the house is being sold, just to name a few of them. As a seller, it is absolutely essential to price the property competitively else it will not attract any buyers. This problem has given rise to multiple companies as well as past research works that try to enhance the predictability of property prices using relevant mathematical models and machine learning techniques. In this research, we investigate the usage of machine learning in predicting the house price based on related estate attributes and visual images. To this end, we collect a dataset of 2,000 houses across different cities in the United States. For each house, we annotate 14 estate attributes and five visual images for exterior, interior-living room, kitchen, bedroom, and bathroom. Following the dataset collection, different features are extracted from the input data. Furthermore, a multi-kernel regression approach is used to predict the house price from both visual cues and estate attributes. The extensive experiments demonstrate the superiority of the proposed method over the baselines.

关键词： House price prediction multi-kernel learning Visual computing Estate attributes Computer vision Machine learning

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