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Found 9 papers, 1 papers with code
Low Rank Multi-Dictionary Selection at Scale
Furthermore, we demonstrate the scalability and quality of LRMDS in both synthetic and real-world datasets and for a range of coding dictionaries.
Multi-Dictionary Tensor Decomposition
In some cases side information is available about the tensor modes.
Unsupervised Instance and Subnetwork Selection for Network Data
We address these challenges within an unsupervised framework for joint subnetwork and instance selection in network data, called UISS, via a convex self-representation objective.
Temporal Scale Estimation for Oversampled Network Cascades: Theory, Algorithms, and Experiment
We highlight one such phenomenon -- temporal distortion -- caused by a misalignment between the rate at which observations of a cascade process are made and the rate at which the process itself operates, and argue that failure to correct for it results in degradation of performance on downstream statistical tasks.
Temporal Graph Signal Decomposition
We propose a general, dictionary-based framework for temporal graph signal decomposition (TGSD).
LinksIQ: Robust and Efficient Modulation Recognition with Imperfect Spectrum Scans
While critical for the practical progress of spectrum sharing, modulation recognition has so far been investigated under unrealistic assumptions: (i) a transmitter's bandwidth must be scanned alone and in full, (ii) prior knowledge of the technology must be available and (iii) a transmitter must be trustworthy.
DSL: Discriminative Subgraph Learning via Sparse Self-Representation
In this work we propose an optimization framework for discriminative subgraph learning (DSL) which simultaneously enforces (i) sparsity, (ii) connectivity and (iii) high discriminative power of the resulting subgraphs of features.
Discriminative Subnetworks with Regularized Spectral Learning for Global-state Network Data
Data mining practitioners are facing challenges from data with network structure.
A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks
In this work, we introduce Social Network Distance (SND) - a distance measure that quantifies the "cost" of evolution of one snapshot of a social network into another snapshot under various models of polar opinion propagation.
