Sentence examples for matrix decomposition methods from inspiring English sources

However, compared to the other matrix decomposition methods, the PCA has several limitations.

All data sets, signal processing and classification methods are common to all the matrix decomposition methods described in the previous section.

To some extend, the results presented in the previous section highlight the strengths and drawbacks of each of the matrix decomposition methods we used in our experiments.

We have published some preliminary experimental results on these databases [10, 11], but this study provides a thorough investigation and comparison of the three matrix decomposition methods mentioned above.

In this study, we investigated the performance of several matrix decomposition methods, such as PCA, NMF and sparse coding when applied for high level feature extraction in the self-taught learning algorithm with respect to the music genre classification task.

The results of this evaluation are shown in Figures 3, 4, 5, and 6 for each training set IS-20, IS-50, IS-100, and ID-250, respectively, compared with the corresponding results obtained using each of the PCA, NMF, and SC data matrix decomposition methods for their best conditions.

The NMF is a Matrix decomposition method [22].

NMF is a matrix decomposition method with nonnegativity constraint.

Then, each of the matrix decomposition method is applied and the respective dictionaries learned.

Then a novel robust control methodology is utilized via a matrix decomposition method.

In [10], an effective matrix decomposition method utilizing cross-correlation matrix is proposed to decorrelate coherent signals.