Using empirical covariance matrix in enhancing prediction accuracy of linear models with missing information
Inference and Estimation in Missing Information (MI) scenarios are important topics in
Statistical Learning Theory and Machine Learning (ML). In ML literature, attempts have been …
Statistical Learning Theory and Machine Learning (ML). In ML literature, attempts have been …
A fast matrix completion method for index coding
We investigate the problem of index coding, where a sender transmits distinct packets over a
shared link to multiple users with side information. The aim is to find an encoding scheme …
shared link to multiple users with side information. The aim is to find an encoding scheme …
Iterative method for simultaneous sparse approximation
S Sadrizadeh, S Kiani, M Boloursaz… - arXiv preprint arXiv …, 2017 - arxiv.org
This paper studies the problem of Simultaneous Sparse Approximation (SSA). This problem
arises in many applications which work with multiple signals maintaining some degree of …
arises in many applications which work with multiple signals maintaining some degree of …
Minimization of the logarithmic function in sparse recovery
C Wang, F Zhou, K Ren, S Ren - Neurocomputing, 2021 - Elsevier
In sparse information recovery, the key issue is to solve the l 0-minimization which is NP-
hard. Therefore we consider the logarithmic alternative function to replace 0-norm. In this …
hard. Therefore we consider the logarithmic alternative function to replace 0-norm. In this …
Generative Model Adversarial Training for Deep Compressed Sensing
A Esmaeili - arXiv preprint arXiv:2106.10696, 2021 - arxiv.org
Deep compressed sensing assumes the data has sparse representation in a latent space,
ie, it is intrinsically of low-dimension. The original data is assumed to be mapped from a low …
ie, it is intrinsically of low-dimension. The original data is assumed to be mapped from a low …
A Novel Approach to Sparse Inverse Covariance Estimation Using Transform Domain Updates and Exponentially Adaptive Thresholding
A Esmaeili, F Marvasti - arXiv preprint arXiv:1811.06773, 2018 - arxiv.org
Sparse Inverse Covariance Estimation (SICE) is useful in many practical data analyses.
Recovering the connectivity, non-connectivity graph of covariates is classified amongst the …
Recovering the connectivity, non-connectivity graph of covariates is classified amongst the …
[PDF][PDF] Information in Missing Patterns: Enhancing Prediction Accuracy in Weighted Linear Regression with Missing Data Using Soft Clustering
A Esmaeili, M Fakharian, YA Abyaneh - Authorea Preprints, 2023 - authorea.com
The linear system with missing information is investigated in this paper. New methods are
introduced to improve the Mean Squared Error (MSE) on the test set in comparison to state …
introduced to improve the Mean Squared Error (MSE) on the test set in comparison to state …
[PDF][PDF] Information in Missing Patterns: Enhancing Prediction Accuracy in Weighted Linear Regression with Missing Data Using Soft Clustering
MA Fakharian, A Esmaeili, YA Abyaneh - techrxiv.org
The linear system with missing information is investigated in this paper. New methods are
introduced to improve the Mean Squared Error (MSE) on the test set in comparison to state …
introduced to improve the Mean Squared Error (MSE) on the test set in comparison to state …
A Convex Similarity Index for Sparse Recovery of Missing Image Samples
This paper investigates the problem of recovering missing samples using methods based on
sparse representation adapted especially for image signals. Instead of $ l_2 $-norm or Mean …
sparse representation adapted especially for image signals. Instead of $ l_2 $-norm or Mean …
New Methods of Enhancing Prediction Accuracy in Linear Models with Missing Data
MA Fakharian, A Esmaeili, F Marvasti - arXiv preprint arXiv:1701.00677, 2017 - arxiv.org
In this paper, prediction for linear systems with missing information is investigated. New
methods are introduced to improve the Mean Squared Error (MSE) on the test set in …
methods are introduced to improve the Mean Squared Error (MSE) on the test set in …