Summary of Label Learning Method Based on Tensor Projection, by Jing Li and Quanxue Gao and Qianqian Wang and Cheng Deng and Deyan Xie
Label Learning Method Based on Tensor Projection
by Jing Li, Quanxue Gao, Qianqian Wang, Cheng Deng, Deyan Xie
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel multi-view clustering approach called Label Learning Method based on Tensor Projection (LLMTP). This method learns cluster labels directly from anchor graphs, avoiding post-processing. LLMTP uses orthogonal projection matrices to project anchor graphs into label spaces and leverages spatial structure information between views through tensor projection. Additionally, the method incorporates tensor Schatten p-norm regularization to ensure consistency across different views. The proposed approach shows effectiveness in extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to group similar data from multiple sources together. It’s called LLMTP, and it helps us find groups without needing extra processing steps. This method uses special projections to get the labels we need and takes into account how different views are related. By making sure the different views agree with each other, this approach can give better results. It works well in real-world tests. |
Keywords
* Artificial intelligence * Clustering * Regularization
