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Feature-Reduction Fuzzy Co-Clustering Algorithm for Hyperspectral Image Segmentation


Class Agnostic Image Common Ob
Abstract


The fuzzy co-clustering algorithms are considered as effective technique for clustering the complex data, such as high-dimensional and large size. In general, features of data objects are considered the same importance. However, in reality, the features have different roles in data analyses; even some of them are considered redundancy in the individual case for data sets. Removing these features is a way for the dimensionality reduction, which needs to improve the performance of data processing algorithms. In this paper, we proposed an improved fuzzy co-clustering algorithm called feature-reduction fuzzy co-clustering (FRFCoC), which can automatically calculate the weight of features and put them out of the data processing. We considered the objective function of the FCoC algorithm with feature-weighted entropy and build a learning procedure for components of the objective function, then reducing the dimension of data by eliminating irrelevant features with small weights. Experiments were conducted on synthetic data sets and hyperspectral image using the robust assessment indexes. Experimental results demonstrated the proposed algorithm outperformed the previous algorithms

KeyWords
Cluster analysis; fuzzy co-clustering; dimensionality reduction; hyperspectral image proccessing



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