1 / 3

Maxeler @ Pupin Institute

Maxeler @ Pupin Institute. Goran Dimić goran.dimic@pupin.rs. DAFNE WP1.7: PARAFAC, ALS. 3-way data tensors: low-rank decomposition In: X: I x J x K Out: A: I x F, B: J x F, C: K x F Identifiability: Unique under mild conditions Repeat: 1. A(X, B, C); 2. B(X, C, A); 3. C(X, A, B);.

lillith-best
Download Presentation

Maxeler @ Pupin Institute

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Maxeler @ Pupin Institute Goran Dimić goran.dimic@pupin.rs

  2. DAFNE WP1.7: PARAFAC, ALS • 3-way data tensors: low-rank decomposition • In: X: I x J x K • Out: A: I x F, B: J x F, C: K x F • Identifiability: Unique under mild conditions • Repeat: 1. A(X, B, C); 2. B(X, C, A); 3. C(X, A, B);

  3. DAFNE WP1.7: Data, Applications • Gastronomy: 42.000 x 350 x 24.000 (UCPH) • Metabolomics: 3.600 x 9.500 x 1.600 (UCPH) • Data size: ~1 TB • Resampling: 1.000x (for data statistics) ~1 PB • Fit each sampling: 1.000 – 100.000 iterations • Equivalent data size: ~1+ EB • Incomplete data: fit if up to 90% missing • Applications: (Hyper)graphs, data mining, SP…

More Related