Name
Abstract | ||
|---|---|---|
Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis. We discuss a robust and scalable algorithm for computing sparse principal component analysis. Specifically, we model SPCA as a matrix factorization problem with orthogonality constraints, and develop specialized optimization algorithms that partially minimize a subset of the variables (variable projection). The framework incorporates a wide variety of sparsity-inducing regularizers for SPCA. We also extend the variable projection approach to robust SPCA, for any robust loss that can be expressed as the Moreau envelope of a simple function, with the canonical example of the Huber loss. Finally, randomized methods for linear algebra are used to extend the approach to the large-scale (big data) setting. The proposed algorithms are demonstrated using both synthetic and real world data. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Machine Learning | Linear algebra,Algorithm,Artificial intelligence,Big data,Optimization problem,Mathematics,Principal component analysis,Machine learning,Scalability |
DocType | Volume | Citations |
Journal | abs/1804.00341 | 1 |
PageRank | References | Authors |
0.43 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| n benjamin erichson | 1 | 26 | 5.69 |
| Peng Zeng | 2 | 3 | 1.85 |
| Krithika Manohar | 3 | 8 | 2.30 |
| S. L. Brunton | 4 | 141 | 23.92 |
| J. Nathan Kutz | 5 | 225 | 47.13 |
| Aleksandr Y. Aravkin | 6 | 252 | 32.68 |