Clustering Center Matrix Research Articles

Dual space-based fuzzy graphs and orthogonal basis clustering for unsupervised feature selection

Unsupervised feature selection (UFS) takes an important position because gaining the class labels is laborious or even impossible. In the domain of UFS, clustering is a major means to exploit label information. The existing methods either could not model a clear clustering structure or could not utilize the local structure of data. Consequently, a new clustering method in combination with graph learning is proposed in this work. Specifically, for clustering, orthogonal basis clustering is introduced, where orthogonal constraints are imposed on the cluster center matrix and the clustering matrix. The clustering indicator matrix is also imposed by a non-negative constraint. A clearer clustering structure and more independent clustering centers are obtained through these constraints. For local preservation, given that traditional graphs for keeping the local manifold are faced with the problem of imbalanced neighbors, the fuzzy graph is introduced to acquire a robust structure, which is applied to both data space and feature space. The topological structure in these spaces can be well maintained. For the choice of salient features, ℓ2,0-norm regularization is imposed on the projection matrix. The object function is solved alternately. Then, a feature selection algorithm is designed. Experiments are designed and performed on nine real-world data sets. The results attest to the effectiveness of the proposed algorithm compared with other relevant algorithms.

GANN: Graph Alignment Neural Network for semi-supervised learning

Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning. Most methods fail to exploit adequate graph information when labeled data is limited, leading to the problem of oversmoothing. To overcome this issue, we propose the Graph Alignment Neural Network (GANN), a simple and effective graph neural architecture. A unique learning algorithm with three alignment rules is proposed to thoroughly explore hidden information for insufficient labels. Firstly, to better investigate attribute specifics, we suggest the feature alignment rule to align the inner product of both the attribute and embedding matrices. Secondly, to properly utilize the higher-order neighbor information, we propose the cluster center alignment rule, which involves aligning the inner product of the cluster center matrix with the unit matrix. Finally, to get reliable prediction results with few labels, we establish the minimum entropy alignment rule by lining up the prediction probability matrix with its sharpened result. Extensive studies on graph benchmark datasets demonstrate that GANN can achieve considerable benefits in semi-supervised node classification and outperform state-of-the-art competitors.

Underdetermined Blind Source Separation Based on Source Number Estimation and Improved Sparse Component Analysis

The signal acquisition process is limited by the installation position and number of sensors in particular types of equipment. Moreover, the observed signals are often compounded by all sources. In order to solve these problems, an underdetermined blind source separation (UBSS) approach with source number estimation and improved sparse component analysis (SCA) is studied. Firstly, the angular probability distribution of scatter as one of measures is obtained in time-frequency (TF) domain based on the sparsity of observations. Meanwhile, the energy sum of each frequency bin as another measure is calculated to eliminate the influence of poor sparsity or non-sparsity. Source number estimation can be obtained by selecting a small peak value between the above two measures. Then, the frequency bins corresponding to these peaks of the energy sum are clustered into two categories, whose first row in cluster center matrix is regarded as the corresponding column of estimated mixing matrix. Finally, the combinatorial algorithm of L1-norm is used to realize the estimation of source signals. Simulation results demonstrate that the proposed method can effectively separate the simulated vibration signals and is more accurate than traditional clustering and hyperplane space methods. Additionally, the natural frequency and damping ratio of modal response can be accurately identified in the test of measured cantilever beam hammering.

Identification of Effective Genes from HIV-1 VPR Microarray Datasets after Constructing GA-Optimized Genetic Network

Identification of effective genes from microarray dataset is important for obtaining relationship between that gene and a particular disease. For this, Gene Regulatory Network (GRN) construction is important. In this paper, GA-optimized GRNs are constructed using radial basis neural network (RBN) approach within cluster center matrix of human HIV-1 Vpr mutant microarray time series gene expression data. After this, effective genes from each microarray dataset are found by calculating minimum Euclidean distance between optimized output value and corresponding dataset.

An Improved FCM Algorithm Based on Subtractive Clustering for Power Load Classification

FCM is used in many power load classification currently, but it also has some shortcomings. This paper give an algorithm based on Subtractive Clustering and improved Fuzzy C-means Clustering (SUB-FCM) to solve this problem. This algorithm use subtractive clustering to initialize the cluster center matrix, solve the random initialization of FCM, and improve the global search ability, avoid falling into local optima. Experimental analysis found this algorithm also could accelerate the convergence speed, and has better clustering results. It can be applied to power load classification effectively.

A Novel Fault Section Location Method Based on Energy Spectrum Entropy of EMD and Fuzzy C-Means Algorithm for Small Current to Ground System

A fault location method of small current to ground system is proposed in this essay. First, the IMF component is obtained through the EMD of the transient zero-mode current at each detection device or FTU when fault occurs. Second, the definition of EMD energy spectrum entropy is put forward to measure the distribution of signal energy in the scale domain, based on which the energy spectrum entropy factor value reflecting the highest frequency IMF component is obtained. Third, FCM algorithm is adopted to cluster analyze the first IMF component spectrum entropy factor from the two feature matrixes of local and global variables. Finally, the fault section is accurately judged through the evaluation of the fuzzy membership degree matrix. In addition, the analysis of global variable clustering center matrix can show the slight differences between the IMF1 power spectrum entropy factor value of health section and that of fault section. A good many simulation calculations show that, this method can accurately and reliably judge fault sections at any time under any grounding resistance value whether it is in the neutral point ungrounded system or arc suppression coil grounded system.

Fault Diagnosis of Metro Vehicle Auxiliary Inverter Based on PSO-KPCM Algorithm

In this paper, the principle of kernel-based possibilistic clustering algorithm and particle swarm optimization algorithm are introduced and the application of the algorithms in fault diagnosis of auxiliary inverter is studied. Several common fault types are simulated by MATALB software. By initializing clustering center of samples based on PSO algorithm, calculating the final membership matrix and the final clustering center matrix based on the KPCM algorithm, the fault samples can be classified finally. The simulation results show that the PSO-KPCM algorithm can be used in the field of fault diagnosis. The PSO-KPCM algorithm even has better results and faster convergence rate than FCM algorithm.

An Improved Fuzzy ISODATA Algorithm for Credit Risk Assessment of the EIT Enterprises

We proposed an improved fuzzy ISODATA algorithm for credit risk assessment of the emerging information technol-ogy enterprise in this paper. Firstly, as the uncertainty of the EIT enterprise is relatively large, we set a reference sample and an initial clustering center matrix so that we overcame the shortcomings of traditional ISODATA algorithm and improved the reliability of fuzzy clustering analysis. Secondly, we proposed the steps of evaluating the EIT enterprises’ credit risk with improved fuzzy ISODATA algorithm. Last but not least, we assessed 10 EIT enterprises’ credit risk of a certain city, which proved the effectiveness and operability.