Summary of Subspace Clustering in Wavelet Packets Domain, by Ivica Kopriva and Damir Sersic
Subspace Clustering in Wavelet Packets Domain
by Ivica Kopriva, Damir Sersic
First submitted to arxiv on: 6 Jun 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 proposed Wavelet Packet (WP) based transform domain subspace clustering method enhances separability of subspaces and robustness to noise by utilizing multiple representations instantiated through subbands. The approach combines original and subband data into a complementary multi-view representation, which is then approximated as a low-rank MERA tensor network. This enables capturing complex intra/inter-view dependencies for improved performance. A self-stopping method selects the subband with the smallest clustering error on the validation set, allowing existing subspace clustering algorithms to be reused. The WP domain approach achieves comparable or better performance than deep subspace clustering algorithms on various image datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to group similar images together using a technique called subspace clustering. This method works by breaking down each image into smaller pieces, like tiny parts of the picture, and then grouping those parts together based on how similar they are. The new approach uses something called wavelet packets to make this process more accurate and robust against noise or errors. The results show that this new method can group images more accurately than some other methods, especially when dealing with complex images like faces or objects. |
Keywords
» Artificial intelligence » Clustering
