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Recently, various clustering approaches were proposed that incorporated both local and global information in an image dataset to learn nonlinear manifold. However, we have to calibrate a number of clustering parameters in these clustering models. In this study, we propose novel less-parameterized Exponential Discriminative Regularization with Nonnegative constraint (NESDR) clustering model based on...
Manifold assumption is that two nearby data points in the high-density region of a low-dimensional data manifold have the same cluster label. Manifold learning based image clustering models are usually employed at local level to deal with nonlinear manifold with an underlying assumption that better the well separated images at local level, the better will be the clustering results. Recently, it has...
Manifold learning based image clustering models are usually employed at local level to deal with images sampled from nonlinear manifold. Usually, gray level image features are used that are obtained by resizing original images through linear interpolation approach. However, significant image variance information is lost in gray level image features. Clustering models that are based on discriminant...
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