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Kernel-based clustering is one of the most popular methods for partitioning nonlinearly separable dataset. However, exhaustive search for the global optimum is NP-hard. Iterative procedure such as k-means can be used to seek one of the local minima. Unfortunately, it is easily trapped into degenerate local minima when the prototypes of clusters are ill-initialized. In this paper, we restate the optimization...
Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered to embed domain-dependent prior knowledge into data-specific kernels, while other forms of prior knowledge were seldom considered in these models. In this paper, we propose a Bayesian maximum margin clustering model (BMMC) based...
Against the low efficiency of training on large-scale SVM, a reduction approach is proposed. This paper presents a new samples reduction method, called bistratal reduction method (BRM). BRM has two levels. The first level is coarse-grained reduction. It deletes the redundant clusters with KDC reduction. The second level is fine-grained reduction. It picks out the support vectors from the clusters...
We introduce a new fuzzy relational clustering technique with Local Scaling Parameter Learning (LSPL). The proposed approach learns the underlying cluster dependent dissimilarity measure while finding compact clusters in the given data set. The learned measure is a Gaussian similarity function defined with respect to each cluster that allows to control the scaling of the clusters and thus, improve...
Recently, semi-supervised clustering has been remarked and discussed in many research fields. In semi-supervised clustering, prior knowledge or information are often formulated as pairwise constraints, that is, must-link and cannot-link. Such pairwise constraints are frequently used in order to improve clustering properties. In this paper, we will propose a new semi-supervised fuzzy c-means clustering...
This paper presents a kernel-based fuzzy c-means algorithm with partition index maximization, called KPIM algorithm. The proposed KPIM algorithm is more robust than the partition index maximization algorithm proposed by Özdemir and Akarum. Experiments show that the advantage of KPIM are robust properties: (1) robust to fuzziness parameter m, (2) robust to outlier, (3) robust to image artifacts; and...
While data clustering has a long history and a large amount of research has been devoted to the development of clustering algorithms, significant challenges still remain. One of the most important challenges in the field is dealing with high dimensional datasets. The class of clustering algorithms that utilises information from Principal Component Analysis has proven very successful in such datasets...
In the last decade, there has been a growing interest in distance function learning for semi-supervised clustering settings. In addition to the earlier methods that learn Mahalanobis metrics (or equivalently, linear transformations), some nonlinear metric learning methods have also been recently introduced. However, these methods either allow limited choice of distance metrics yielding limited flexibility...
In this paper, the method that measuring dataset of knitted yarns is clustered using improving fuzzy kernel c-Means (FKCM) clustering algorithm is proposed. In FKCM clustering algorithm, the data of low dimension input space is mapped to high dimension feature space, FCM clustering algorithm is performed in feature space, then the constraint optimization distance matrix and membership matrix of testing...
In order to improve the training efficiency to the data set, an improved adaptive Support Vector Machine (SVM) algorithm with combinational Fuzzy C-means Clustering is proposed. With multi-layer fuzzy C-means clustering algorithm original data are pretreated to remove the training data, which has no contribution to the classification. The remaining data are used to complete the training work for SVM...
Against the low efficiency of training on large-scale SVM, a reduction approach based on kernel distance clustering is proposed. The kernel distance's formulation is brought in to cluster the highly-dimensioned dataset, and the clustering step will reduce a large amount of unsupport vectors during training, thereby, the training time will decrease. The experiments show that this new training algorithm...
In this paper, we adopt a differential-geometry viewpoint to tackle the problem of learning a distance online. As this problem can be cast into the estimation of a fixed-rank positive semidefinite (PSD) matrix, we develop algorithms that exploits the rich geometry structure of the set of fixed-rank PSD matrices. We propose a method which separately updates the subspace of the matrix and its projection...
A fast fractal encoding algorithm based on ant colony algorithm is proposed to reduce coding time. The algorithm produces a completely identical fractal encoding to that of the conventional full search in reduced time. Using ant-based clustering algorithm and kernel method, we propose in this paper a kernel function clustering based on ant colony algorithm. It automatically realizes classification...
Cluster analysis is one of the several important tools in modern data analysis, and the clustering can be regarded as an optimization problem. The underlying assumption is that there are natural tendencies of cluster or group structure in the data and the goal is to be able to uncover this structure. In general, traditional clustering algorithms are suitable to implement clustering only if the feature...
For classification problem clustering method divides the dataset into many clusters based on correlation attribute of all elements of the dataset. How to classify data within the same clustering number as close as possible and data in different clusters as depart as possible is the key of clustering method. Clustering support vector machines (ClusterSVM) partition the training data into disjoint clusters...
In this paper, in order to reduce the support vectors on a large scale data set, we train support vector machines which utilize the hyper-spheres as the training samples. By representing adjacent samples of the same class as hyper-spheres, the boundary location can be controlled both by the center and radius of the hyper-spheres. We demonstrate that the optimization problem in this condition can be...
This paper researches the possibility of using locally weighted algorithm for intelligent modeling of a nonlinear system for vanadium extraction in metallurgical process and proposes some optimized methods by finding the optimized regression coefficients by gradient descent and kernel function bandwidth by weighted distance. But kernel matrix computation for high dimensional data source demands heavy...
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