We introduce normalized nonnegative models (NNM) for explorative data analysis. NNMs are partial convexifications of models from probability theory. We demonstrate their value at the example of item recommendation. We show that NNM-based recommender systems satisfy three criteria that all recommender systems should ide…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,181 papers · 148 categories
Trend · papers per month
5 results for “NNM”
Paper studies -consistency of nearest neighbor matching for biased samples.
problem Biased sampling and missing data in statistical problems.
method Nearest neighbor matching (NNM) to estimate population quantities from biased samples.
result NNM is -consistent in finite dimensions without smoothness and boundedness assumptions.
Nuclear norm minimization (NNM) has recently gained significant attention for its use in rank minimization problems. Similar to compressed sensing, using null space characterizations, recovery thresholds for NNM have been studied in \cite{arxiv,Recht_Xu_Hassibi}. However simulations show that the thresholds are far fro…
Paper introduces a new approach for matrix completion with improved convergence guarantees.
problem Matrix completion problems, especially in the presence of probabilistic dependency.
method Leave-one-out analysis combined with PGD and NNM for convergence analysis.
result Established convergence guarantees for PGD and optimal sample complexity for NNM.
In recent years, the nuclear norm minimization (NNM) problem has been attracting much attention in computer vision and machine learning. The NNM problem is capitalized on its convexity and it can be solved efficiently. The standard nuclear norm regularizes all singular values equally, which is however not flexible enou…