Abstract
The ". locally linear reconstruction" (LLR) provides a principled and k-insensitive way to determine the weights of k-nearest neighbor (k-NN) learning. LLR, however, does not provide a confidence interval for the k neighbors-based reconstruction of a query point, which is required in many real application domains. Moreover, its fixed linear structure makes the local reconstruction model unstable, resulting in performance fluctuation for regressions under different k values. Therefore, we propose a probabilistic local reconstruction (PLR) as an extended version of LLR in the k-NN regression. First, we probabilistically capture the reconstruction uncertainty by incorporating Gaussian regularization prior into the reconstruction model. This prevents over-fitting when there are no informative neighbors in the local reconstruction. We then project data into a higher dimensional feature space to capture the non-linear relationship between neighbors and a query point when a value of k is large. Preliminary experimental results demonstrated that the proposed Bayesian kernel treatment improves accuracy and k-invariance. Moreover, from the experiment on a real virtual metrology data set in the semiconductor manufacturing, it was found that the uncertainty information on the prediction outcomes provided by PLR supports more appropriate decision making.
Original language | English |
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Pages (from-to) | 427-439 |
Number of pages | 13 |
Journal | Neurocomputing |
Volume | 131 |
DOIs | |
Publication status | Published - 2014 May 5 |
Externally published | Yes |
Bibliographical note
Funding Information:This work was supported by the Brain Korea 21 project in 2006-2011, the Brain Korea 21 PLUS project in 2013, the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2011-0030814), Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT, and Future Planning (2011-0021893) and 2013 Seoul National University Brain Fusion Program Research Grant. This work was also supported by the Engineering Research Institute of SNU.
Keywords
- Bayesian kernel model
- K-NN regression
- Locally linear reconstruction
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence