Unsupervised dimensionality reduction for hyperspectral imagery via local geometric structure feature learning
G Shi, H Huang, L Wang - IEEE Geoscience and Remote …, 2019 - ieeexplore.ieee.org
Hyperspectral images (HSIs) possess a large number of spectral bands, which easily lead to
the curse of dimensionality. To improve the classification performance, a huge challenge is …
the curse of dimensionality. To improve the classification performance, a huge challenge is …
Supervised low-rank embedded regression (SLRER) for robust subspace learning
M Wan, Y Yao, T Zhan, G Yang - IEEE Transactions on Circuits …, 2021 - ieeexplore.ieee.org
Locality-preserving projection (LPP) has been widely used in feature extraction. However,
LPP does not use data category information and uses the-norm for distance measurement …
LPP does not use data category information and uses the-norm for distance measurement …
Social media: New perspectives to improve remote sensing for emergency response
Remote sensing is a powerful technology for Earth observation (EO), and it plays an
essential role in many applications, including environmental monitoring, precision …
essential role in many applications, including environmental monitoring, precision …
Low-rank sparse preserving projections for dimensionality reduction
Learning an efficient projection to map high-dimensional data into a lower dimensional
space is a rather challenging task in the community of pattern recognition and computer …
space is a rather challenging task in the community of pattern recognition and computer …
A new fusion approach for content based image retrieval with color histogram and local directional pattern
In this paper, we propose a novel color image retrieval approach by using an effective fusion
of two types of histograms extracted from color and local directional pattern (LDP) …
of two types of histograms extracted from color and local directional pattern (LDP) …
Solving partial least squares regression via manifold optimization approaches
Partial least squares regression (PLSR) has been a popular technique to explore the linear
relationship between two data sets. However, all existing approaches often optimize a PLSR …
relationship between two data sets. However, all existing approaches often optimize a PLSR …
Transferable discriminant linear regression for cross-corpus speech emotion recognition
Speech emotion recognition (SER) has attracted much interest recently due to its wide
applications. However, it should be noted that most SER methods are conducted on the …
applications. However, it should be noted that most SER methods are conducted on the …
Random weight network-based fuzzy nonlinear regression for trapezoidal fuzzy number data
This paper proposes a random weight network (RWN)-based fuzzy nonlinear regression
(FNR) model, abbreviated as TraFNR RWN, to solve the FNR problem in which both inputs …
(FNR) model, abbreviated as TraFNR RWN, to solve the FNR problem in which both inputs …
Sparse non-negative transition subspace learning for image classification
Recently many variations of least squares regression (LSR) have been developed to
address the problem of over-fitting that widely exists in the task of image classification …
address the problem of over-fitting that widely exists in the task of image classification …
ECG-based biometrics using a deep network based on independent component analysis
In this study, we propose a systematic deep network based on independent component
analysis (ICA) called ICANet1, which is subsequently modified into an improved model …
analysis (ICA) called ICANet1, which is subsequently modified into an improved model …