Sentence examples for matrix factorization framework from inspiring English sources
Fig. 2 Matrix factorization framework.
We detail a matrix factorization framework that can incorporate different domain information through various parameters of the matrix factorization method.
Subsequently, we explain our matrix factorization framework (MFF) that offers a pragmatic framework of incorporating many real-world physical constraints.
In [15], authors proposed a matrix factorization framework to build two mapping matrices for the training images and the auxiliary text data.
Constraints are discussed in "Matrix factorization" section, and examples of hyperspectral imaging and MF-based images analysis are presented in "Matrix factorization framework (MFF)" and "Domain specific applications" sections.
We presented a matrix factorization framework to implement different physical constraints such as sparsity, spatial smoothness, and non-negativity to constrain the unmixing, leading to more meaningful and interpretable endmembers and abundance maps.
Methods based on the non-negative matrix factorization (NMF) framework have been widely used for various speech processing tasks.
A general nonnegative matrix factorization (NMF) framework decomposes spectra of reverberated speech in to those of the clean and room impulse response filter.
Finally, Symmetric Non-negative Matrix Factorization (SNMF) framework is utilized to assign roads to proper clusters with high intra-similarity and low inter-similarity.
We introduce the popular linear unmixing techniques principal component analysis (PCA) and non-negative matrix factorization (NMF) under this framework and finally, discuss the examples of the two real-world constraints, sparsity and spatial smoothness, as preferential soft constraints with non-negativity on endmembers.
Here, we provide an overview of the framework for understanding matrix factorization ("low-rank approximation") and tuning the various parameters on this framework for day-to-day needs of handling different domain observations.
