Loading...

Using empirical covariance matrix in enhancing prediction accuracy of linear models with missing information

Moradipari, A ; Sharif University of Technology

744 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/SAMPTA.2017.8024338
  3. Abstract:
  4. Inference and Estimation in Missing Information (MI) scenarios are important topics in Statistical Learning Theory and Machine Learning (ML). In ML literature, attempts have been made to enhance prediction through precise feature selection methods. In sparse linear models, LASSO is well-known in extracting the desired support of the signal and resisting against noisy systems. When sparse models are also suffering from MI, the sparse recovery and inference of the missing models are taken into account simultaneously. In this paper, we will introduce an approach which enjoys sparse regression and covariance matrix estimation to improve matrix completion accuracy, and as a result enhancing feature selection preciseness which leads to reduction in prediction Mean Squared Error (MSE). We will compare the effect of employing covariance matrix in enhancing estimation accuracy to the case it is not used in feature selection. Simulations show the improvement in the performance as compared to the case where the covariance matrix estimation is not used. © 2017 IEEE
  5. Keywords:
  6. Feature selection ; Linear model ; Feature extraction ; Forecasting ; Learning algorithms ; Learning systems ; Matrix algebra ; Mean square error ; Covariance matrix estimation ; Feature selection methods ; Linear modeling ; Matrix completion ; Missing information ; Prediction accuracy ; Prediction mean-squared errors ; Statistical learning theory ; Covariance matrix
  7. Source: 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017, 3 July 2017 through 7 July 2017 ; 2017 , Pages 446-450 ; 9781538615652 (ISBN)
  8. URL: https://ieeexplore.ieee.org/document/8024338