Abstract
As dimensionality is very high, image feature space is usually complex. For effectively processing this space, technology of dimensionality reduction is widely used. Semi-supervised clustering incorporates limited information into unsupervised clustering in order to improve clustering performance. However, many existing semi-supervised clustering methods can not be used to handle high-dimensional sparse data. To solve this problem, we proposed a semi-supervised fuzzy clustering method via constrained orthogonal projection. With results of experiments on different datasets, it shows the method has good clustering performance for handling high dimensionality data.
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