Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
S Basith, B Manavalan, T Hwan Shin… - Medicinal research …, 2020 - Wiley Online Library
Discovery and development of biopeptides are time‐consuming, laborious, and dependent
on various factors. Data‐driven computational methods, especially machine learning (ML) …
on various factors. Data‐driven computational methods, especially machine learning (ML) …
Overview and comparative study of dimensionality reduction techniques for high dimensional data
S Ayesha, MK Hanif, R Talib - Information Fusion, 2020 - Elsevier
The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …
capabilities are leading towards huge volume of data. The dimensions of data indicate the …
Spectral methods for data science: A statistical perspective
Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
Comparison of freshly squeezed, Non-thermally and thermally processed orange juice based on traditional quality characters, untargeted metabolomics, and volatile …
K Wang, Z Xu - Food Chemistry, 2022 - Elsevier
The NOVA food classification system, divides foods into four categories, namely
unprocessed and minimally processed foods, processed culinary ingredients, processed …
unprocessed and minimally processed foods, processed culinary ingredients, processed …
Inference and uncertainty quantification for noisy matrix completion
Noisy matrix completion aims at estimating a low-rank matrix given only partial and
corrupted entries. Despite remarkable progress in designing efficient estimation algorithms …
corrupted entries. Despite remarkable progress in designing efficient estimation algorithms …
[HTML][HTML] Bridging convex and nonconvex optimization in robust PCA: Noise, outliers, and missing data
This paper delivers improved theoretical guarantees for the convex programming approach
in low-rank matrix estimation, in the presence of (1) random noise,(2) gross sparse outliers …
in low-rank matrix estimation, in the presence of (1) random noise,(2) gross sparse outliers …
User-friendly covariance estimation for heavy-tailed distributions
We provide a survey of recent results on covariance estimation for heavy-tailed distributions.
By unifying ideas scattered in the literature, we propose user-friendly methods that facilitate …
By unifying ideas scattered in the literature, we propose user-friendly methods that facilitate …
Singular vector and singular subspace distribution for the matrix denoising model
Singular vector and singular subspace distribution for the matrix denoising model Page 1
The Annals of Statistics 2021, Vol. 49, No. 1, 370–392 https://doi.org/10.1214/20-AOS1960 © …
The Annals of Statistics 2021, Vol. 49, No. 1, 370–392 https://doi.org/10.1214/20-AOS1960 © …
How to reduce dimension with PCA and random projections?
In our “big data” age, the size and complexity of data is steadily increasing. Methods for
dimension reduction are ever more popular and useful. Two distinct types of dimension …
dimension reduction are ever more popular and useful. Two distinct types of dimension …
Biwhitening reveals the rank of a count matrix
Estimating the rank of a corrupted data matrix is an important task in data analysis, most
notably for choosing the number of components in principal component analysis. Significant …
notably for choosing the number of components in principal component analysis. Significant …