It is necessary to use multivariate SFA instead of univariate SFA for separating multi-dimensional signals.This paper makes use of Regularized Sparse Kernel SFA(RSKSFA) instead of multivariate SFA and applies it to the problem of blind source separation in particular to image separation.
For small but complex data sets the kernel SFA approach leads to over-fitting and numerical instabilities.
To enforce a stable solution, we introduce regularization to the SFA objective.
Experimental results show that the proposed approach can improve prediction results.
Visit for more related articles at International Journal of Innovative Research in Computer and Communication Engineering Advances in digital image processing were increased in the past few years.
Here the kernel trick is used in combination with sparsification to provide a powerful function class for large data sets.
Sparsity is achieved by a novel matching pursuit approach that can be applied to other tasks as well.Recently, they are also applied in e-learning tasks such as recommending resources (e.g. In this work, we propose a novel approach which uses recommender system techniques for educational data mining, especially for predicting student performance.To validate this approach, we compare recommender system techniques with traditional regression methods such as logistic/linear regression by using educational data for intelligent tutoring systems.If set to log likelihood and with binary ratings, the recommender implements a simple version Menon and Elkan's LFL model, which predicts binary labels, has no advanced regularization, and uses no side information.This recommender makes use of multi-core machines if requested.Regularization based on rating frequency Regularization proportional to the inverse of the square root of the number of ratings associated with the user or item. Use 'naive' parallelization strategy instead of conflict-free 'distributed' SGD The exact sequence of updates depends on the thread scheduling. when setting –random-seed=N, do NOT set this property.