Cucumber disease recognition based on Global-Local Singular value decomposition
S Zhang, Z Wang - Neurocomputing, 2016 - Elsevier
Plant leaf based plant disease recognition is becoming an important research topic in the
pattern recognition and image processing. Many conventional methods are not effective for …
pattern recognition and image processing. Many conventional methods are not effective for …
A novel regularized asymmetric non-negative matrix factorization for text clustering
MH Aghdam, MD Zanjani - Information Processing & Management, 2021 - Elsevier
Non-negative matrix factorization (NMF) is a dimension reduction method that extracts
semantic features from high-dimensional data. Most of the developed optimization methods …
semantic features from high-dimensional data. Most of the developed optimization methods …
Weighted sparse coding regularized nonconvex matrix regression for robust face recognition
Most existing regression based classification methods for robust face recognition usually
characterize the representation error using L 1-norm or Frobenius-norm for the pixel-level …
characterize the representation error using L 1-norm or Frobenius-norm for the pixel-level …
Kernel robust singular value decomposition
EAL Neto, PC Rodrigues - Expert Systems with Applications, 2023 - Elsevier
Singular value decomposition (SVD) is one of the most widely used algorithms for
dimensionality reduction and performing principal component analysis, which represents an …
dimensionality reduction and performing principal component analysis, which represents an …
Discriminative multi-scale sparse coding for single-sample face recognition with occlusion
The single sample per person (SSPP) face recognition is a major problem and it is also an
important challenge for practical face recognition systems due to the lack of sample data …
important challenge for practical face recognition systems due to the lack of sample data …
Generalized low-rank approximation of matrices based on multiple transformation pairs
S Ahmadi, M Rezghi - Pattern Recognition, 2020 - Elsevier
Dimensionality reduction is a critical step in the learning process that plays an essential role
in various applications. The most popular methods for dimensionality reduction, SVD and …
in various applications. The most popular methods for dimensionality reduction, SVD and …
Surface roughness evaluation in hardened materials by pattern recognition using network theory
M Babič, M Calì, I Nazarenko, C Fragassa… - International Journal on …, 2019 - Springer
Performance characteristics of the products made of metallic materials such as wear
resistance, fatigue strength, stability of gaps and strain between the connections, corrosion …
resistance, fatigue strength, stability of gaps and strain between the connections, corrosion …
Block sparse representation for pattern classification: theory, extensions and applications
By exploiting the low-dimensional structure of high-dimensional data, sparse representation
based classifiers (SRC) has recently attracted massive attention in pattern recognition. In …
based classifiers (SRC) has recently attracted massive attention in pattern recognition. In …
Low-rank approximation-based bidirectional linear discriminant analysis for image data
X Chen, T Chen - Multimedia Tools and Applications, 2024 - Springer
Dimensionality reduction methods for images directly without matrix-to-vector conversion
have been widely concerned and achieved good classification results, especially for face …
have been widely concerned and achieved good classification results, especially for face …
Wind field reconstruction using dimension-reduction of CFD data with experimental validation
Short-term wind forecasting is important in updating wind electricity trading strategies, facility
protection and more effective operation control. Physical based models, particularly those …
protection and more effective operation control. Physical based models, particularly those …