• This article is all about a recently-viewed paper, Multi-Aspect Streaming Tensor Completion in proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘17).
  • #tensor_completion, #MAST, #dynamic_tensor_decomposition,
#online_algorithm, #streaming_data, #CP_factorization
• Abstract
  • Completion of incremental tensors without sacrificing effectiveness remains a challenging task due to the uncertainty of tensor mode changes and complex data structure of multi-aspect streaming tensors. To bridge this gap, we propose a Multi-Aspect Streaming Tensor completion framework based on CANDECOMP/PARAFAC decomposition to track the subspace of general incremental tensors for completion.
• Relevance to My Research
  • MAST is the 1st approach to online analysis that solved the multi-aspect streaming problem.
  • I’m also working on streaming tensor and trying to develop my method. You can see the progress of my research on GitHub.

More other topics on https://datalab.snu.ac.kr/seminar.

This is a part of image pre-processing module for MopReM: Moiré Pattern Removal for Mobile, Texts/Diagrams on Single-colored Background. Click the link to visit the project page of SNU-CV-PIP team.

We first select the window to screen-capture and crop the shared screen with range selector. The module saves the screenshot surrounded with target frame as target.png. Re-capture the target photo and save as source.png. Only executable on desktop!

Screen Share Image Crop

PONE-D-21-39294R1
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How can we accurately and efficiently decompose a tensor stream? The problem of efficiently decomposing tensor streams has been of great interest because many real-world data dynamically change over time. Therefore, we propose DAO-CP, an accurate and efficient online CP decomposition method which adapts to data changes.