Search Results for author:
Found 7 papers, 3 papers with code
Maximal Volume Matrix Cross Approximation for Image Compression and Least Squares Solution
Our main results consist of an improvement of a classic estimate for matrix cross approximation and a greedy approach for finding the maximal volume submatrices.
Semi-supervised Local Cluster Extraction by Compressive Sensing
Local clustering problem aims at extracting a small local structure inside a graph without the necessity of knowing the entire graph structure.
A Compressed Sensing Based Least Squares Approach to Semi-supervised Local Cluster Extraction
A least squares semi-supervised local clustering algorithm based on the idea of compressed sensing is proposed to extract clusters from a graph with known adjacency matrix.
Compressive Sensing for cut improvement and local clustering
We show how one can phrase the cut improvement problem for graphs as a sparse recovery problem, whence one can use algorithms originally developed for use in compressive sensing (such as SubspacePursuit or CoSaMP) to solve it.
Information Theory Numerical Analysis Social and Information Networks Information Theory Numerical Analysis 68Q25, 68R10, 68U05, 94A12
A Compressive Sensing Approach to Community Detection with Applications
The community detection problem for graphs asks one to partition the n vertices V of a graph G into k communities, or clusters, such that there are many intracluster edges and few intercluster edges.
Stochastic Coordinate Coding and Its Application for Drosophila Gene Expression Pattern Annotation
The effectiveness of gene expression pattern annotation relies on the quality of feature representation.
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion
Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.
