5 accepted papers
Given two sets of variables, derived from a common set of samples, sparse Canonical Correlation Analysis (CCA) seeks linear combinations of a small number of variables in each set, such that the induced \emphcanonical variables are maximally correlated. Sparse CCA is NP-hard. We propose a novel comb…
In Bipartite Correlation Clustering (BCC) we are given a complete bipartite graph G with ’+’ and ’-’ edges, and we seek a vertex clustering that maximizes the number of agreements: the number of all ’+’ edges within clusters plus all ’-’ edges cut across clusters. BCC is known to be NP-hard [5]. W…
Orthogonal Nonnegative Matrix Factorization {(ONMF)} aims to approximate a nonnegative matrix as the product of two $k$-dimensional nonnegative factors, one of which has orthonormal columns. It yields potentially useful data representations as superposition of disjoint parts, while it has been shown…
We consider the following multi-component sparse PCA problem:given a set of data points, we seek to extract a small number of sparse components with \emph{disjoint} supports that jointly capture the maximum possible variance.Such components can be computed one by one, repeatedly solving the single-c…
We introduce a variant of (sparse) PCA in which the set of feasible support sets is determined by a graph. In particular, we consider the following setting: given a directed acyclic graph G on p vertices corresponding to variables, the non-zero entries of the extracted principal component must coinc…