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Found 6 papers, 1 papers with code
Modeling and Mining Multi-Aspect Graphs With Scalable Streaming Tensor Decomposition
How can we identify dynamic patterns in those graphs, and how can we deal with streaming data, when the volume of data to be processed is very large?
Adaptive Granularity in Tensors: A Quest for Interpretable Structure
Data collected at very frequent intervals is usually extremely sparse and has no structure that is exploitable by modern tensor decomposition algorithms.
OCTen: Online Compression-based Tensor Decomposition
Tensor decompositions are powerful tools for large data analytics as they jointly model multiple aspects of data into one framework and enable the discovery of the latent structures and higher-order correlations within the data.
t-PINE: Tensor-based Predictable and Interpretable Node Embeddings
Contrary to baseline methods, which generally learn explicit graph representations by solely using an adjacency matrix, t-PINE avails a multi-view information graph, the adjacency matrix represents the first view, and a nearest neighbor adjacency, computed over the node features, is the second view, in order to learn explicit and implicit node representations, using the Canonical Polyadic (a. k. a.
Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition
In this paper, we define "concept" and "concept drift" in the context of streaming tensor decomposition, as the manifestation of the variability of latent concepts throughout the stream.
SamBaTen: Sampling-based Batch Incremental Tensor Decomposition
In this paper we introduce SaMbaTen, a Sampling-based Batch Incremental Tensor Decomposition algorithm, which incrementally maintains the decomposition given new updates to the tensor dataset.
