Supervised Principal Component Analysis Via Manifold Optimization
| Autor: | Chandra Sripada, Daniel Kessler, Clayton Scott, Alexander Ritchie, Laura Balzano |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Dimensionality reduction Variation (game tree) High dimensional Manifold optimization Machine learning computer.software_genre Outcome (probability) Regression Task (project management) ComputingMethodologies_PATTERNRECOGNITION Principal component analysis Artificial intelligence business computer |
| Zdroj: | DSW |
| Popis: | High dimensional prediction problems are pervasive in the scientific community. In practice, dimensionality reduction (DR) is often performed as an initial step to improve prediction accuracy and in-terpretability. Principal component analysis (PCA) has been utilized extensively for DR, but does not take advantage of outcome variables inherent in the prediction task. Existing approaches for supervised PCA (SPCA) either take a multi-stage approach or incorporate supervision indirectly. We present a manifold optimization approach to SPCA that simultaneously solves the prediction and dimensionality reduction problems. The proposed framework is general enough for both regression and classification settings. Our empirical results show that the proposed approach explains nearly as much variation as PCA while outperforming existing methods in prediction accuracy. |
| Databáze: | OpenAIRE |
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