Summary of Improving Lsh Via Tensorized Random Projection, by Bhisham Dev Verma and Rameshwar Pratap
Improving LSH via Tensorized Random Projection
by Bhisham Dev Verma, Rameshwar Pratap
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
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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 proposes novel locality sensitive hashing (LSH) algorithms for approximate nearest neighbor search in tensor data. The existing LSH methods for vector data are impractical for higher-order tensors due to the exponential increase in parameter size. To address this issue, the authors develop two new LSH methods: CP-E2LSH, TT-E2LSH, and CP-SRP, TT-SRP, which leverage CP and tensor train decompositions techniques. These approaches are space-efficient and can be applied efficiently to low-rank tensors. The paper provides a theoretical analysis of their correctness and efficacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating new tools for computers to find the closest matches in big datasets that use complicated math, like tensors. Right now, these tools get too slow or take up too much memory when dealing with super-high-dimensional data. To fix this, the researchers invented two new ways to make LSH work better: CP-E2LSH and TT-E2LSH (CP-SRP and TT-SRP are for other types of math). These new methods use special techniques that help keep things simple and fast. |
Keywords
* Artificial intelligence * Nearest neighbor
