| Autor: |
Georgina Cosma, T. Martin Mcginnity |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 7, Pp 107400-107412 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2019.2932868 |
| Popis: |
Eigendecomposition is the factorization of a matrix into its canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors. A common step is the reduction of the data to a kernel matrix also known as a Gram matrix which is used for machine learning tasks. A significant drawback of kernel methods is the computational complexity associated with manipulating kernel matrices. This paper demonstrates that leading eigenvectors derived from singular value decomposition (SVD) and Nyström approximation methods can be utilized for classification tasks without the need to construct Gram matrices. Experiments were conducted with 14 biomedical datasets to compare classifier performance when taking as input into a classifier matrices containing: 1) leading eigenvectors which result from each approximation method, and 2) matrices which result from constructing the patient-by-patient Gram matrix. The results provide evidence to support the main hypothesis of this paper that using the leading eigenvectors as input into a classifier significantly (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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