In many real-world applications, data are represented by high-dimensional features. Despite the simplicity, existing K-means subspace clustering algorithms often employ eigenvalue decomposition to generate an approximate solution, which makes the model less efficiency. Besides, their loss functions are either sensitive to outliers or small loss errors. In this paper, we propose a fast adaptive K-means (FAKM) type subspace clustering model, where an adaptive loss function is designed to provide a flexible cluster indicator calculation mechanism, thereby suitable for datasets under different distributions. To find the optimal feature subset, FAKM performs clustering and feature selection simultaneously without the eigenvalue decomposition, therefore efficient for real-world applications. We exploit an efficient alternative optimization algorithm to solve the proposed model, together with theoretical analyses on its convergence and computational complexity. Finally, extensive experiments on several benchmark datasets demonstrate the advantages of FAKM compared to state-of-the-art clustering algorithms.
The aims of this study were to explore the feasibility of using LIBS technology combined with a hybrid RF algorithm for quantitative analysis of K in potash ore.
In pattern recognition and data mining, clustering is a classical technique to group matters of interest and has been widely employed to numerous applications. Among various clustering algorithms, K-means (KM) clustering is most popular for its simplicity and efficiency. However, with the rapid development of the social network, high-dimensional data are frequently generated, which poses a considerable challenge to the traditional KM clustering as the curse of dimensionality. In such scenarios, it is difficult to directly cluster such high-dimensional data that always contain redundant features and noises. Although the existing approaches try to solve this problem using joint subspace learning and KM clustering, there are still the following limitations: 1) the discriminative information in low-dimensional subspace is not well captured; 2) the intrinsic geometric information is seldom considered; and 3) the optimizing procedure of a discrete cluster indicator matrix is vulnerable to noises. In this paper, we propose a novel clustering model to cope with the above-mentioned challenges. Within the proposed model, discriminative information is adaptively explored by unifying local adaptive subspace learning and KM clustering. We extend the proposed model using a robust l2,1-norm loss function, where the robust cluster centroid can be calculated in a weighted iterative procedure. We also explore and discuss the relationships between the proposed algorithm and several related studies. Extensive experiments on kinds of benchmark data sets demonstrate the advantage of the proposed model compared with the state-of-the-art clustering approaches.
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