The Equivalence Between Principal Component Analysis and Nearest Flat in the Least Square Sense
- First Online:
- Cite this article as:
- Shao, YH. & Deng, NY. J Optim Theory Appl (2015) 166: 278. doi:10.1007/s10957-014-0647-y
- 167 Downloads
In this paper, we declare the equivalence between the principal component analysis and the nearest q-flat in the least square sense by showing that, for given m data points, the linear manifold with nearest distance is identical to the linear manifold with largest variance. Furthermore, from this observation, we give a new simpler proof for the approach to find the nearest q-flat.