Summary of Salsa: Sequential Approximate Leverage-score Algorithm with Application in Analyzing Big Time Series Data, by Ali Eshragh and Luke Yerbury and Asef Nazari and Fred Roosta and Michael W. Mahoney
SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data
by Ali Eshragh, Luke Yerbury, Asef Nazari, Fred Roosta, Michael W. Mahoney
First submitted to arxiv on: 30 Dec 2023
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 The proposed SALSA algorithm uses methods from randomized numerical linear algebra to efficiently approximate leverage scores for large matrices, achieving high accuracy within a constant factor plus epsilon. This leads to an improved ARMA model fitting algorithm, LSARMA, which guarantees maximum likelihood estimates and has significantly better running times in big data scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SALSA algorithm is designed to accurately approximate large matrix leverage scores using RandNLA methods, leading to efficient and effective ARMA model fitting with large-scale time series data. This new approach outperforms existing methods in terms of computational complexity and numerical accuracy, making it a valuable tool for big data applications. |
Keywords
* Artificial intelligence * Likelihood * Time series
