Sparse principal component analysis (PCA) imposes extra constraints or penalty terms to the standard PCA to achieve sparsity. In this paper, we first introduce an efficient algor...
In a typical realistic scenario, there exist some past data about the structure of the network which are analyzed with respect to some possibly future spreading process, such as b...
Mayank Lahiri, Arun S. Maiya, Rajmonda Sulo, Habib...
This paper describes the development of a predictive model for corporate insolvency risk in Australia. The model building methodology is empirical with out-ofsample future year te...
Document clustering techniques mostly rely on single term analysis of the document data set, such as the Vector Space Model. To better capture the structure of documents, the unde...
: Weexemplify in this paper, howa discovery system is applied to the analysis of simulation experimentsin practical political planning, andshowwhatkind of newknowledgecan be discov...