Since LH is a positive semi-definite matrix, this problem is a typical Regularized Least Square (RLS) problem that can be effi- ciently solved. We obtain the partial derivative of Equa- tion 10 with respect to B
Difference equations describing the convergence behavior of this algorithm in Gaussian inputs and additive contaminated Gaussian noises are derived, from which new expressions for the steady-state excess mean square error (EMSE) are obtained. They suggest that regularization can help to reduce the ...
Several algorithms based on the least-mean square (LMS) (Gu et al. 2009; Chen et al. 2009) and the recursive least squares (RLS) (Babadi et al. 2010; Angelosante et al. 2010; Eksioglu 2011; Eksioglu and Tanc 2011) techniques have been reported with different penalty or shrinkage ...
Finally, we use the example of SAG for solving least square regression to demonstrate the benefit of data preconditioning. Similar analysis carries on to other variance reduced stochastic optimization algorithms (Johnson and Zhang 2013; Shalev-Shwartz and Zhang 2013). When \(\lambda =1/n\) the ...
James-Stein estimators are another well-known class of shrinkage estimators that use a biased estimator to achieve a smaller mean square error for a multidimensional parameter [10]. James-Stein estimators generally assume a (1 - f(y))*y form; in general, a simple prescriptive form for ...
The package LASSOPACK implements penalized regression methods: LASSO, elastic net, ridge, square-root LASSO, adaptive LASSO. uses fast path-wise coordinate descent algorithms three commands for three different penalization approaches: cross-validation (cvlasso), information criteria (lasso2) and '...
regularized least square method [20]. Their experiments on publicly available GEP datasets have shown that MSRC is efficient for cancer classification and can achieve higher accuracy than many existing representative schemes such as SVM, SRC and least absolute shrinkage and selection operator (LASSO) ...
experiments on the commercial modular aero-propulsion system simulation (C-MAPSS) show that our model provides competitive performance in terms of Root-Mean-Square Error (RMSE) and Score metrics. Compared to the state-of-the-art method based onRecurrent Neural Network(RNN) or CNN and its variant...
Kriegel, H.P., Kröger, P., Schubert, E., Zimek, A.: A general framework for increasing the robustness of PCA-based correlation clustering algorithms. In: Scientific and Statistical Database Management. Lecture Notes in Computer Science, vol. 5069, pp. 418–435 (2008) Kwak, N.: Princip...
• MvSL methods outperform other algorithms under all cases. On the one hand, MvSL methods do not need the datasets obey gaussian distribution; on the other hand, MvSL methods utilize the partial label information to construct a graph embedding framework, which encour- aged items of the ...