A5101410889- Face and Expression Recognition
- Neural Networks and Applications
- Brain Tumor Detection and Classification
- Image and Signal Denoising Methods
- Sparse and Compressive Sensing Techniques
- Advanced Image Fusion Techniques
- Advanced Computational Techniques and Applications
- Advanced Image and Video Retrieval Techniques
- Advanced Fluorescence Microscopy Techniques
- Advanced Sensor and Control Systems
- Computational Physics and Python Applications
- Human Pose and Action Recognition
- Gene expression and cancer classification
- Simulation and Modeling Applications
- Advanced Measurement and Detection Methods
- Optical Coherence Tomography Applications
- Image Retrieval and Classification Techniques
- Remote-Sensing Image Classification
- Digital Imaging for Blood Diseases
- Advanced Algorithms and Applications
- Anomaly Detection Techniques and Applications
- Algorithms and Data Compression
- Image and Video Stabilization
- Machine Learning and ELM
- Tensor decomposition and applications
Zhejiang University of Technology
2011-2025
Zhejiang University of Science and Technology
2013
Feature is important for many applications in biomedical signal analysis and living system analysis. A fast discriminative stochastic neighbor embedding (FDSNE) method feature extraction proposed this paper by improving the existing DSNE method. The algorithm adopts an alternative probability distribution model constructed based on its<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>-nearest neighbors from interclass...
Dimensionality reduction is an important issue for numerous applications including biomedical images analysis and living system analysis. Neighbor embedding, those representing the global local structure as well dealing with multiple manifolds, such elastic embedding techniques, can go beyond traditional dimensionality methods find better optima. Nevertheless, existing neighbor algorithms not be directly applied in classification suffering from several problems: (1) high computational...
A new method for performing a nonlinear form of manifold-oriented stochastic neighbor projection is proposed. By the use kernel functions, one can operate in feature space without ever computing coordinates data that space, but rather by simply inner products between images all pairs space. The proposed termed as kernel-based manifoldoriented projection(KMSNP). two different strategies, KMSNP divided into methods: KMSNP1 and KMSNP2. Experimental results on several databases show that,...
摘要: 已有投影算法都直接通过完整的输入训练集求解最佳变换矩阵，难以进行增量式学习扩展。针对此问题，该文通过组合优化策略提出局部判别投影方法应用于分类问题。该算法同时包括类间判别信息和类内局部保持特征，求得的变换矩阵还具有正交性。此外，利用核函数将算法扩展至非线性应用，使之可以适应更多的数据类型。在ORL人脸库和小样本说话人辨认应用中验证了该算法的有效性。 关键词: 模式识别 / 局部判别投影 组合优化策略 核函数 子空间学习
In image denoising (IDN) processing, the low-rank property is usually considered as an important prior. As a convex relaxation approximation of low rank, nuclear norm based algorithms and their variants have attracted significant attention. These can be collectively called domain methods, whose common drawback requirement great number iterations for some acceptable solution. Meanwhile, sparsity images in certain transform has also been exploited problems. Sparsity learning achieve extremely...
In image denoising (IDN) processing, the low-rank property is usually considered as an important prior. As a convex relaxation approximation of low rank, nuclear norm-based algorithms and their variants have attracted significant attention. These can be collectively called domain-based methods whose common drawback requirement great number iterations for some acceptable solution. Meanwhile, sparsity images in certain transform domain has also been exploited problems. Sparsity learning...