Single-cell transcriptional analysis reveals ILC-like cells in zebrafish
Innate lymphoid cells (ILCs) are important mediators of the immune response and
homeostasis in barrier tissues of mammals. However, the existence and function of ILCs in …
homeostasis in barrier tissues of mammals. However, the existence and function of ILCs in …
[图书][B] Multilinear subspace learning: dimensionality reduction of multidimensional data
H Lu, KN Plataniotis, A Venetsanopoulos - 2013 - books.google.com
Due to advances in sensor, storage, and networking technologies, data is being generated
on a daily basis at an ever-increasing pace in a wide range of applications, including cloud …
on a daily basis at an ever-increasing pace in a wide range of applications, including cloud …
Robust principal component analysis based on fuzzy local information reservation
Y Gao, X Wang, J Xie, J Pan, P Yan… - IEEE transactions on …, 2024 - ieeexplore.ieee.org
Principal Component Analysis (PCA) aims to acquire the principal component space
containing the essential structure of data, instead of being used for mining and extracting the …
containing the essential structure of data, instead of being used for mining and extracting the …
Simple exponential family PCA
Bayesian principal component analysis (BPCA), a probabilistic reformulation of PCA with
Bayesian model selection, is a systematic approach to determining the number of essential …
Bayesian model selection, is a systematic approach to determining the number of essential …
Image outlier detection and feature extraction via L1-norm-based 2D probabilistic PCA
This paper introduces an L1-norm-based probabilistic principal component analysis model
on 2D data (L1-2DPPCA) based on the assumption of the Laplacian noise model. The …
on 2D data (L1-2DPPCA) based on the assumption of the Laplacian noise model. The …
Modified principal component analysis: An integration of multiple similarity subspace models
We modify the conventional principal component analysis (PCA) and propose a novel
subspace learning framework, modified PCA (MPCA), using multiple similarity …
subspace learning framework, modified PCA (MPCA), using multiple similarity …
Dimensionality reduction for hyperspectral data based on class-aware tensor neighborhood graph and patch alignment
Y Gao, X Wang, Y Cheng… - IEEE transactions on …, 2014 - ieeexplore.ieee.org
To take full advantage of hyperspectral information, to avoid data redundancy and to
address the curse of dimensionality concern, dimensionality reduction (DR) becomes …
address the curse of dimensionality concern, dimensionality reduction (DR) becomes …
Matrix-based vs. vector-based linear discriminant analysis: A comparison of regularized variants on multivariate time series data
J Zhao, H Liang, S Li, Z Yang, Z Wang - Information Sciences, 2024 - Elsevier
Over the past two decades, matrix-based or bilinear discriminant analysis (BLDA) methods
have received much attention. However, it has been reported that the traditional vector …
have received much attention. However, it has been reported that the traditional vector …
Mixtures of skewed matrix variate bilinear factor analyzers
MPB Gallaugher, PD McNicholas - Advances in data analysis and …, 2020 - Springer
In recent years, data have become increasingly higher dimensional and, therefore, an
increased need has arisen for dimension reduction techniques for clustering. Although such …
increased need has arisen for dimension reduction techniques for clustering. Although such …
Modified minimum squared error algorithm for robust classification and face recognition experiments
In this paper, we improve the minimum squared error (MSE) algorithm for classification by
modifying its classification rule. Differing from the conventional MSE algorithm which first …
modifying its classification rule. Differing from the conventional MSE algorithm which first …