Summary of Sparse Tensor Pca Via Tensor Decomposition For Unsupervised Feature Selection, by Junjing Zheng et al.
Sparse Tensor PCA via Tensor Decomposition for Unsupervised Feature Selection
by Junjing Zheng, Xinyu Zhang, Weidong Jiang
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Recently, researchers have been exploring the integration of Tensor Decomposition (TD) methods into unsupervised feature selection (UFS), leveraging the benefits of tensor structures in capturing relationships between different modes. However, existing methods only focus on minimizing reconstruction errors, failing to fully utilize the interpretable and discriminative information in factor matrices. To address this, we propose two Sparse Tensor Principal Component Analysis (STPCA) models that exploit projection directions in factor matrices for UFS. The first model extends Tucker Decomposition to a multiview sparse regression form, while the second model formulates a sparse version of Tensor Singular Value Decomposition (T-SVD). Both models are transformed into individual convex subproblems, which we prove converge using Hermitian Positive Semidefinite Cone (HPSD) projection. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of our proposed methods in handling various data tensor scenarios, outperforming state-of-the-art UFS methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a superpower that lets you identify patterns and relationships within huge amounts of data! Researchers have been working on developing new techniques to make this possible. They’re using something called Tensor Decomposition (TD) to help with this process, but they realized it wasn’t being used to its full potential. So, they created two new methods that use TD in a different way, allowing them to find important patterns and features within the data. These methods are really good at handling different types of data and perform better than existing techniques. This is an exciting breakthrough with many potential applications! |
Keywords
» Artificial intelligence » Feature selection » Principal component analysis » Regression » Unsupervised
