A semi-supervised clustering via orthogonal projection
Cui Peng, Rubo Zhang · 2009
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.