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Clustering with constraints is an active area in machine learning and data mining. In this paper, a semi-supervised kernel-based fuzzy C-means algorithm called PCKFCM is proposed which incorporates both semi-supervised learning technique and the kernel method into traditional fuzzy clustering algorithm. The clustering is achieved by minimizing a carefully designed objective function. A kernel-based...
Time series clustering finds applications in diverse fields of science and technology. Kernel based clustering methods like kernel K-means method need number of clusters as input and cannot handle outliers or noise. In this paper, we propose a density based clustering method in kernel feature space for clustering multivariate time series data of varying length. This method can also be used for clustering...
Kernel k-means is an extension of the standard k-means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, in this work we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage through a global search...
Object recognition and categorization are considered as fundamental steps in the vision based navigation for inspection robot as it must plan its behaviors based on various kinds of obstacles detected from the complex background. However, current approaches typically require some amount of supervision, which is viewed as a expensive burden and restricted to relatively small number of applications...
Effective use of support vector machines (SVMs) in classification necessitates the appropriate choice of a kernel. Designing problem specific kernels involves the definition of a similarity measure, with the condition that kernels are positive semi-definite (PSD). An alternative approach which places no such restrictions on the similarity measure is to construct a set of inputs and let each example...
Locally linear embedding heavily depends on whether the neighborhood graph represents the underlying geometry structure of the data manifolds. Inspired from the cognitive law, the relative transformation(RT) and kernel relative transformation (KRT) are proposed. They can improve the distinction between data points and inhibit the impact of noise and sparsity of data, which can be then applied to construct...
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