English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

What learning algorithms can be run directly on compressively-sensed data? Inthis work, we consider the question of accurately and efficiently computinglow-rank matrix or tensor factorizations given data compressed via randomprojections. We examine the approach of first performing factorization in thecompressed domain, and then reconstructing the original high-dimensionalfactors from the recovered (compressed) factors. In both the matrix and tensorsettings, we establish conditions under which this natural approach willprovably recover the original factors. While it is well-known that randomprojections preserve a number of geometric properties of a dataset, our workcan be viewed as showing that they can also preserve certain solutions ofnon-convex, NP-Hard problems like non-negative matrix factorization. We supportthese theoretical results with experiments on synthetic data and demonstratethe practical applicability of compressed factorization on real-world geneexpression and EEG time series datasets.

Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data