Results 21 to 30 of about 24,916,518 (300)
Economic Predictions With Big Data: The Illusion of Sparsity [PDF]
Econometrica, 2018 We compare sparse and dense representations of predictive models in macroeconomics, microeconomics, and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse model, but on a wide set of models ...
Giannone, Domenico +2 more
openaire +5 more sources
On the Sparsity of LASSO Minimizers in Sparse Data Recovery
Constructive Approximation, 2022 We present a detailed analysis of the unconstrained $\ell_1$-weighted LASSO method for recovery of sparse data from its observation by randomly generated matrices, satisfying the Restricted Isometry Property (RIP) with constant $δ<1$, and subject to negligible measurement and compressibility errors.
Foucart, Simon +2 more
openaire +3 more sources
Sparsity-Constrained Distributed Unmixing of Hyperspectral Data [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019 one column, 17 pages, 10 figures ...
Khoshsokhan S. +2 more
openaire +3 more sources
Sparsity Measure and the Detection of Significant Data
, 2009 The paper provides a formal description of the sparsity of a representation via the detection thresholds. The formalism proposed derives from theoretical results about the detection of significant coefficients when data are observed in presence of additive white Gaussian noise.
Atto, Abdourrahmane +2 more
core +3 more sources
CMB data analysis and sparsity [PDF]
Statistical Methodology, 2008 The statistical analysis of the soon to come Planck satellite CMB data will help set tighter bounds on major cosmological parameters. On the way, a number of practical difficulties need to be tackled, notably that several other astrophysical sources emit radiation in the frequency range of CMB observations.
Abrial, P. +5 more
openaire +4 more sources
Research on BP Neural Network Recommendation Model Fusing User Reviews and Ratings
IEEE Access, 2021 Sparsity of rating data is a severe problem to be solved in modern recommendation researches. The fusion recommendation method is an effective solution for the problem.
Heyong Wang, Ming Hong, Zhenqin Hong
doaj +1 more source
Sparsity-based audio declipping methods: selected overview, new algorithms, and large-scale evaluation [PDF]
, 2021 International audienceRecent advances in audio declipping have substan- tially improved the state of the art. Yet, practitioners need guidelines to choose a method, and while existing benchmarks have been instrumental in advancing the field, larger-scale
Gribonval, Rémi +7 more
core +1 more source
Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest Recommendation
Sensors, 2023 A personalized point-of-interest (POI) recommender system is of great significance to facilitate the daily life of users. However, it suffers from some challenges, such as trustworthiness and data sparsity problems.
Jingmin An, Wei Jiang, Guanyu Li
doaj +1 more source
Wavelet shrinkage using adaptive structured sparsity constraints [PDF]
, 2015 Structured sparsity approaches have recently received much attention in the statistics, machine learning, and signal processing communities. A common strategy is to exploit or assume prior information about structural dependencies inherent in the data ...
Tomassi, Diego Rodolfo +5 more
core +1 more source
A Novel Implicit Trust Recommendation Approach for Rating Prediction
IEEE Access, 2020 Rating predictions, as an application that is widely used in recommender systems, have gradually become a valuable way which can help user narrow down their choices quickly and make wise decisions from the vast amount of information.
Yakun Li +3 more
doaj +1 more source