Pith. sign in

REVIEW

Multi-View Treelet Transform

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1606.00800 v2 pith:FB3WYTUI submitted 2016-06-02 stat.ML cs.CVcs.SIq-bio.NC

classification stat.MLcs.CVcs.SIq-bio.NC
keywords datahierarchicalmulti-viewtransformtreeletcurrentmethodsstructure
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Current multi-view factorization methods make assumptions that are not acceptable for many kinds of data, and in particular, for graphical data with hierarchical structure. At the same time, current hierarchical methods work only in the single-view setting. We generalize the Treelet Transform to the Multi-View Treelet Transform (MVTT) to allow for the capture of hierarchical structure when multiple views are available. Further, we show how this generalization is consistent with the existing theory and how it might be used in denoising empirical networks and in computing the shared response of functional brain data.

Discussion (0). Continue with ORCID to comment.

Pith tools