Traditional clustering algorithms consider all of the dimensions of an input data set equally. However, in the high dimensional data, a common property is that data points are highly clustered in subspaces, which means classes of objects are categorized in subspaces rather than the entire space. Subspace clustering is an extension of traditional clustering that seeks to find clusters in differe...

Subspace clustering is the problem of clustering data that lie close to a union of linear subspaces. Existing algebraic subspace clustering methods are based on fitting the data with an algebraic variety and decomposing this variety into its constituent subspaces. Such methods are well suited to the case of a known number of subspaces of known and equal dimensions, where a single polynomial van...

In this paper, a large amount of different financial and macroeconomic variables are used to predict the U.S. recession periods. We propose new cost-sensitive extension gradient boosting model, which can take into account class imbalance problem binary response variable. The imbalance, caused by scarcity periods in our application, is that emphasized with high-dimensional datasets. Our empirica...

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 25 March 2021Accepted: 07 September 2021Published online: 30 November 2021KeywordsANOVA, high-dimensional, approximation, intrepretability, fast Fourier methodsAMS Subject Headings65T, 42B05, 62-07, 65D15Publication DataISSN (online): 2577-0187Publisher: Society for Indus...