Dec 17, 2014 · Following recent contributions in non-linear sparse represen- tations, this work focuses on a particular non-linear model, defined as the nested ...

Recalling that most linear sparse representation algorithms can be straight- forwardly extended to non-linear models, we emphasize that their performance highly ...

Following recent contributions in non-linear sparse represen-tations, this work focuses on a particular non-linear model, defined as the nested composition ...

Oct 22, 2024 · Recalling that most linear sparse representation algorithms can be straightforwardly extended to non-linear models, we emphasize that their ...

Oct 16, 2015 · I'm wondering what is the best way to handle sparse+non-sparse data in eg a Ridge regression using scikit learn. Ridge can handle both sparse and nonsparse ...

Data-driven Markov linear models of nonlinear fluid flows using maps of the state into a sparse feature space are explored in this article.

In this article, we will develop some rigorous results showing that it can be possible to find optimally sparse representations by efficient techniques in ...

Missing: nested | Show results with:nested

Learning sparse representations on data adaptive dictionaries is a state-of-the-art method for modeling data. But when the dictionary is large and the data ...

In this chapter, we discuss efficient representations of your data, so that it takes less memory. The fundamental idea, is that if your data is sparse,