Results 71 to 80 of about 1,415,945 (160)
Sparse Matrix Factorization [PDF]
, 2014 We investigate the problem of factorizing a matrix into several sparse matrices and propose an algorithm for this under randomness and sparsity assumptions.
Neyshabur, Behnam, Panigrahy, Rina
core
Sparse Coding on Stereo Video for Object Detection [PDF]
, 2017 Deep Convolutional Neural Networks (DCNN) require millions of labeled training examples for image classification and object detection tasks, which restrict these models to domains where such datasets are available.
Kenyon, Garrett T. +2 more
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Sparse Image Reconstruction using Sparse Priors [PDF]
2006 International Conference on Image Processing, 2006 Sparse image reconstruction is of interest in the fields of radioastronomy and molecular imaging. The observation is assumed to be a linear transformation of the image, and corrupted by additive white Gaussian noise. We study the usage of sparse priors in the empirical Bayes framework: it permits the selection of the hyperparameters of the prior in a ...
Michael Ting +2 more
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Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing
Remote Sensing, 2018 Hyperspectral unmixing, aiming to estimate the fractional abundances of pure spectral signatures in each mixed pixel, has attracted considerable attention in analyzing hyperspectral images.
Si Wang +4 more
doaj +1 more source
Structured, Sparse Aggregation [PDF]
Journal of the American Statistical Association, 2012 We introduce a method for aggregating many least squares estimator so that the resulting estimate has two properties: sparsity and structure. That is, only a few candidate covariates are used in the resulting model, and the selected covariates follow some structure over the candidate covariates that is assumed to be known a priori.
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Positive definite estimation of large covariance matrix using generalized nonconvex penalties
IEEE Access, 2016 This paper addresses the issue of large covariance matrix estimation in a high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarantee have been developed.
Fei Wen +3 more
doaj +1 more source
Robust and Sparse Regression via γ-Divergence
Entropy, 2017 In high-dimensional data, many sparse regression methods have been proposed. However, they may not be robust against outliers. Recently, the use of density power weight has been studied for robust parameter estimation, and the corresponding divergences ...
Takayuki Kawashima, Hironori Fujisawa
doaj +1 more source
Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning
In Federated Learning (FL), both client resource constraints and communication costs pose major problems for training large models. In the centralized setting, sparse training addresses resource constraints, while in the distributed setting, local training addresses communication costs.
Meinhardt, Georg +3 more
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Proceedings of the Edinburgh Mathematical Society, 2012 AbstractWe define sparse saturated fusion systems and show that, for odd primes, sparse systems are constrained. This simplifies the proof of the Glauberman–Thompson p-Nilpotency Theorem for fusion systems and a related theorem of Stellmacher. We then define a more restrictive class of saturated fusion systems, called extremely sparse systems, that are
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Sparse Bayesian Registration [PDF]
, 2014 We propose a Sparse Bayesian framework for non-rigid registration. Our principled approach is flexible, in that it efficiently finds an optimal, sparse model to represent deformations among any preset, widely overcomplete range of basis functions. It addresses open challenges in state-of-the-art registration, such as the automatic joint estimate of ...
Le Folgoc, Loic +3 more
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