Summary of Coseparable Nonnegative Tensor Factorization with T-cur Decomposition, by Juefei Chen et al.
Coseparable Nonnegative Tensor Factorization With T-CUR Decomposition
by Juefei Chen, Longxiu Huang, Yimin Wei
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses a crucial issue in unsupervised learning by introducing Nonnegative Tensor Factorization (NTF), an extension of Nonnegative Matrix Factorization (NMF). NMF is a popular method for extracting meaningful features from data, but it has limitations when applied to high-dimensional data like images or videos. The authors propose a novel approach that leverages tensors and the tensor t-product to retain inherent correlations in the data. They develop an alternating index selection method for coseparable core representation and integrate it with the tensor Discrete Empirical Interpolation Method (t-DEIM) for randomized index selection. Experimental results on synthetic and facial analysis datasets demonstrate the efficiency of coseparable NTF compared to coseparable NMF. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to analyze big data, like pictures or videos. Currently, we can’t easily find important features in this type of data using traditional methods. The researchers are trying to solve this problem by creating a new tool that uses tensors (a special kind of array) and a technique called coseparable Nonnegative Tensor Factorization (NTF). This method helps us identify patterns and structures in the data more effectively than before. The authors tested their approach on fake data and real facial recognition datasets, showing it’s faster and better at finding important features. |
Keywords
* Artificial intelligence * Unsupervised
