Unsupervised Domain Adaptation (UDA) aims to learn a classifier for the unlabeled target domain by leveraging knowledge from labeled source with different but related distribution. Many existing approaches typically domain-invariant representation space directly matching marginal distributions of two domains. However, they ignore exploring underlying discriminative features data and align cross...

Clustering high-dimensional data is a challenging task in data mining, and clustering high-dimensional categorical data is even more challenging because it is more difficult to measure the similarity between categorical objects. Most algorithms assume feature independence when computing similarity between data objects, or make use of computationally demanding techniques such as PCA for numerica...

A Feature selection for the high dimensional data clustering is a difficult problem because the ground truth class labels that can guide the selection are unavailable in clustering. Besides, the data may have a broad number of features and the irrelevant ones can run the clustering. A novel feature weighting scheme is proposed, in which the weight for each feature is a measure of its contributi...