Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features
In the area of machine learning and pattern recognition, object classification is getting an
attraction due to its range of applications such as visual surveillance. In recent times …
attraction due to its range of applications such as visual surveillance. In recent times …
Learning salient and discriminative descriptor for palmprint feature extraction and identification
Palmprint recognition has been widely applied in security and, particularly, authentication. In
the past decade, various palmprint recognition methods have been proposed and achieved …
the past decade, various palmprint recognition methods have been proposed and achieved …
Learning latent low-rank and sparse embedding for robust image feature extraction
Z Ren, Q Sun, B Wu, X Zhang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
To defy the curse of dimensionality, the inputs are always projected from the original high-
dimensional space into the target low-dimension space for feature extraction. However, due …
dimensional space into the target low-dimension space for feature extraction. However, due …
Minimum entropy principle guided graph neural networks
Graph neural networks (GNNs) are now the mainstream method for mining graph-structured
data and learning low-dimensional node-and graph-level embeddings to serve downstream …
data and learning low-dimensional node-and graph-level embeddings to serve downstream …
Latent linear discriminant analysis for feature extraction via isometric structural learning
Linear discriminant analysis (LDA) is one of the most successful feature extraction methods,
which projects high-dimensional data to a low-dimensional space with discriminative …
which projects high-dimensional data to a low-dimensional space with discriminative …
Robust sparse low-rank embedding for image dimension reduction
Many methods based on matrix factorization have recently been proposed and achieve
good performance in many practical applications. Latent low-rank representation (LatLRR) …
good performance in many practical applications. Latent low-rank representation (LatLRR) …
Learning complete and discriminative direction pattern for robust palmprint recognition
Palmprint direction patterns have been widely and successfully used in palmprint
recognition methods. Most existing direction-based methods utilize the pre-defined filters to …
recognition methods. Most existing direction-based methods utilize the pre-defined filters to …
Low-rank discriminative least squares regression for image classification
Discriminative least squares regression (DLSR) aims to learn relaxed regression labels to
replace strict zero-one labels. However, the distance of the labels from the same class can …
replace strict zero-one labels. However, the distance of the labels from the same class can …
Low-rank inter-class sparsity based semi-flexible target least squares regression for feature representation
Least squares regression (LSR) is an important machine learning method for feature
extraction, feature selection, and image classification. For the training samples, there are …
extraction, feature selection, and image classification. For the training samples, there are …
Low-rank constraint based dual projections learning for dimensionality reduction
L Jiang, X Fang, W Sun, N Han, S Teng - Signal processing, 2023 - Elsevier
Subspace learning is a widely-used fundamental method for feature extraction in several
fields. Existing subspace-based methods only concentrate on projecting all data into a …
fields. Existing subspace-based methods only concentrate on projecting all data into a …