Mutual kNN based spectral clustering

The key step of spectral clustering is learning the affinity matrix to measure the similarity among data points. This paper proposes a new spectral clustering method, which uses mutual k nearest neighbor to obtain the affinity matrix by removing the influence of noise. Then, the characteristics of high-dimensional data are self-represented to ensure local important information of data by using affinity matrix in standardized processing. Furthermore, we also use the normalization method to further improve the performance of clustering. Experimental analysis on eight benchmark data sets showed that our proposed method outperformed the state-of-the-art clustering methods in terms of clustering performance such as cluster accuracy and normalized mutual information.

[1]  Xiaofeng Zhu,et al.  Unsupervised feature selection via local structure learning and sparse learning , 2017, Multimedia Tools and Applications.

[2]  Xiaodong Li,et al.  Missing value imputation methods for TCM medical data and its effect in the classifier accuracy , 2017, 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom).

[3]  Shuicheng Yan,et al.  Robust and Efficient Subspace Segmentation via Least Squares Regression , 2012, ECCV.

[4]  Xuelong Li,et al.  Block-Row Sparse Multiview Multilabel Learning for Image Classification , 2016, IEEE Transactions on Cybernetics.

[5]  Zi Huang,et al.  A Sparse Embedding and Least Variance Encoding Approach to Hashing , 2014, IEEE Transactions on Image Processing.

[6]  Xingyu Wang,et al.  Author's Personal Copy Biomedical Signal Processing and Control Lasso Based Stimulus Frequency Recognition Model for Ssvep Bcis , 2022 .

[7]  Xiaofeng Zhu,et al.  A Novel Dynamic Hyper-graph Inference Framework for Computer Assisted Diagnosis of Neuro-Diseases , 2017, IPMI.

[8]  Beilun Wang,et al.  Kernelized Information-Theoretic Metric Learning for Cancer Diagnosis Using High-Dimensional Molecular Profiling Data , 2016, ACM Trans. Knowl. Discov. Data.

[9]  A. Cichocki,et al.  A novel BCI based on ERP components sensitive to configural processing of human faces , 2012, Journal of neural engineering.

[10]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[11]  Honggang Zhang,et al.  Low-rank and structured sparse subspace clustering , 2016, 2016 Visual Communications and Image Processing (VCIP).

[12]  Jiye Liang,et al.  A novel fuzzy clustering algorithm with between-cluster information for categorical data , 2013, Fuzzy Sets Syst..

[13]  Gang Niu,et al.  Information-Maximization Clustering Based on Squared-Loss Mutual Information , 2014, Neural Computation.

[14]  Weilan Wang,et al.  Local consistent low rank representation for image clustering , 2016, 2016 Chinese Control and Decision Conference (CCDC).

[15]  Tianrui Li,et al.  An Improved Cop-Kmeans Clustering for Solving Constraint Violation Based on MapReduce Framework , 2013, Fundam. Informaticae.

[16]  H. L. Shashirekha,et al.  Gene selection by Mutual Nearest Neighbor approach , 2015, 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT).

[17]  Dinggang Shen,et al.  Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers , 2017, IEEE Transactions on Big Data.

[18]  Heng Tao Shen,et al.  Hierarchical Multi-Clue Modelling for POI Popularity Prediction with Heterogeneous Tourist Information , 2019, IEEE Transactions on Knowledge and Data Engineering.

[19]  Feiping Nie,et al.  The Constrained Laplacian Rank Algorithm for Graph-Based Clustering , 2016, AAAI.

[20]  Ulrike von Luxburg,et al.  How the result of graph clustering methods depends on the construction of the graph , 2011, ArXiv.

[21]  Heng Tao Shen,et al.  Video Captioning With Attention-Based LSTM and Semantic Consistency , 2017, IEEE Transactions on Multimedia.

[22]  Xiaofeng Zhu,et al.  Local and Global Structure Preservation for Robust Unsupervised Spectral Feature Selection , 2018, IEEE Transactions on Knowledge and Data Engineering.

[23]  Peter Trebuna,et al.  The importance of normalization and standardization in the process of clustering , 2014, 2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI).

[24]  Saeid Nahavandi,et al.  Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering , 2014, Journal of Neuroscience Methods.

[25]  Xuelong Li,et al.  Graph PCA Hashing for Similarity Search , 2017, IEEE Transactions on Multimedia.

[26]  Xiaoping Yang,et al.  A method of recognition based on the feature layer fusion of palmprint and hand vein , 2013, Other Conferences.

[27]  Hongjie Jia,et al.  Spectral Clustering with Neighborhood Attribute Reduction Based on Information Entropy , 2014, J. Comput..

[28]  Xiaofeng Zhu,et al.  Efficient kNN Classification With Different Numbers of Nearest Neighbors , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Xuelong Li,et al.  Learning k for kNN Classification , 2017, ACM Trans. Intell. Syst. Technol..

[30]  Xiaofeng Zhu,et al.  Graph self-representation method for unsupervised feature selection , 2017, Neurocomputing.

[31]  Xiaofeng Zhu,et al.  Unsupervised feature selection by self-paced learning regularization , 2020, Pattern Recognit. Lett..

[32]  Dinggang Shen,et al.  A novel relational regularization feature selection method for joint regression and classification in AD diagnosis , 2017, Medical Image Anal..

[33]  Shichao Zhang,et al.  Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[34]  R. Vidal,et al.  Sparse Subspace Clustering: Algorithm, Theory, and Applications. , 2013, IEEE transactions on pattern analysis and machine intelligence.

[35]  Danijela Cabric,et al.  Unsupervised frequency clustering algorithm for null space estimation in wideband spectrum sharing networks , 2017, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[36]  Xiaofeng Zhu,et al.  Dynamic graph learning for spectral feature selection , 2018, Multimedia Tools and Applications.

[37]  Shichao Zhang,et al.  Low-Rank Sparse Subspace for Spectral Clustering , 2019, IEEE Transactions on Knowledge and Data Engineering.

[38]  Nicu Sebe,et al.  A Distance-Computation-Free Search Scheme for Binary Code Databases , 2016, IEEE Transactions on Multimedia.

[39]  Luis Mendoza,et al.  Trans‐Proteomic Pipeline, a standardized data processing pipeline for large‐scale reproducible proteomics informatics , 2015, Proteomics. Clinical applications.

[40]  Keqiu Li,et al.  Optimized big data K-means clustering using MapReduce , 2014, The Journal of Supercomputing.

[41]  Lingfeng Wang,et al.  MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multicategory Classification , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[42]  Shichao Zhang,et al.  Noisy data elimination using mutual k-nearest neighbor for classification mining , 2012, J. Syst. Softw..

[43]  Nicu Sebe,et al.  Optimized Graph Learning Using Partial Tags and Multiple Features for Image and Video Annotation , 2016, IEEE Transactions on Image Processing.

[44]  Xiaofeng Zhu,et al.  Personalized Diagnosis for Alzheimer's Disease , 2017, MICCAI.

[45]  Nicu Sebe,et al.  Quantization-based hashing: a general framework for scalable image and video retrieval , 2018, Pattern Recognit..

[46]  Nathan D. Cahill,et al.  CUTS WITH SOFT MUST-LINK CONSTRAINTS FOR IMAGE SEGMENTATION AND CLUSTERING , 2014 .

[47]  薛志祥 Xue Zhixiang,et al.  Local Hypergraph Laplacian Regularized Low-Rank Representation for Noise Reduction of Hyperspectral Images , 2017 .

[48]  Zili Zhang,et al.  Missing Value Estimation for Mixed-Attribute Data Sets , 2011, IEEE Transactions on Knowledge and Data Engineering.

[49]  Simon Lucey,et al.  Convolutional Sparse Coding for Trajectory Reconstruction , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  R. Harikumar,et al.  Performance analysis of KNN classifier and K-means clustering for robust classification of epilepsy from EEG signals , 2016, 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[51]  Xiaofeng Zhu,et al.  One-Step Multi-View Spectral Clustering , 2019, IEEE Transactions on Knowledge and Data Engineering.