Nacionalni portal odprte znanosti

Many clustering methods partition the data groups based on the input data similarity matrix. Thus, the clustering results highly depend on the data similarity learning. Because the similarity measurement and data clustering are often conducted in two separated steps, the learned data similarity may ...

Most semi-supervised learning models propagate the labels over the Laplacian graph, where the graph should be built beforehand. However, the computational cost of constructing the Laplacian graph matrix is very high. On the other hand, when we do classification, data points lying around the decision ...

Feature selection is one of the most important data mining research topics with many applications. In practical problems, features often have group structure to effect the outcomes. Thus, it is crucial to automatically identify homogenous groups of features for high-dimensional data analysis. Octago ...