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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We study the problem of subspace tracking in the presence of missing data(ST-miss). In recent work, we studied a related problem called robust ST. Inthis work, we show that a simple modification of our robust ST solution alsoprovably solves ST-miss and robust ST-miss. To our knowledge, our result is thefirst `complete' guarantee for ST-miss. This means that we can prove that underassumptions on only the algorithm inputs, the output subspace estimates areclose to the true data subspaces at all times. Our guarantees hold under mildand easily interpretable assumptions, and allow the underlying subspace tochange with time in a piecewise constant fashion. In contrast, all existingguarantees for ST are partial results and assume a fixed unknown subspace.Extensive numerical experiments are shown to back up our theoretical claims.Finally, our solution can be interpreted as a provably correct mini-batch andmemory-efficient solution to low-rank Matrix Completion (MC).

Provable Subspace Tracking from Missing Data and Matrix Completion