Overview and comparative study of dimensionality reduction techniques for high dimensional data
S Ayesha, MK Hanif, R Talib - Information Fusion, 2020 - Elsevier
The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …
capabilities are leading towards huge volume of data. The dimensions of data indicate the …
Big data dimensionality reduction techniques in IoT: Review, applications and open research challenges
In the age of big data, all forms of data with increasing samples and high-dimensional
characteristics are demonstrating their importance in a variety of fields, including data …
characteristics are demonstrating their importance in a variety of fields, including data …
Simultaneous feature weighting and parameter determination of neural networks using ant lion optimization for the classification of breast cancer
In this paper, feature weighting is used to develop an effective computer-aided diagnosis
system for breast cancer. Feature weighting is employed because it boosts the classification …
system for breast cancer. Feature weighting is employed because it boosts the classification …
Discriminative and geometry-preserving adaptive graph embedding for dimensionality reduction
Learning graph embeddings for high-dimensional data is an important technology for
dimensionality reduction. The learning process is expected to preserve the discriminative …
dimensionality reduction. The learning process is expected to preserve the discriminative …
Breast cancer detection based on feature selection using enhanced grey wolf optimizer and support vector machine algorithms
Breast cancer is the leading cause of high fatality among women population. Identification of
the benign and malignant tumor at correct time plays a critical role in the diagnosis of breast …
the benign and malignant tumor at correct time plays a critical role in the diagnosis of breast …
Localized multiple kernel learning via sample-wise alternating optimization
Y Han, K Yang, Y Ma, G Liu - IEEE transactions on cybernetics, 2013 - ieeexplore.ieee.org
Our objective is to train support vector machines (SVM)-based localized multiple kernel
learning (LMKL), using the alternating optimization between the standard SVM solvers with …
learning (LMKL), using the alternating optimization between the standard SVM solvers with …
Kernel ridge regression with lagged-dependent variable: Applications to prediction of internal bond strength in a medium density fiberboard process
N Kim, YS Jeong, MK Jeong… - IEEE Transactions on …, 2012 - ieeexplore.ieee.org
Medium density fiberboard (MDF) is one of the most popular products in wood composites
industry. Kernel-based regression approaches such as the support vector machine for …
industry. Kernel-based regression approaches such as the support vector machine for …
Kernel self-optimization learning for kernel-based feature extraction and recognition
JB Li, YH Wang, SC Chu, JF Roddick - Information Sciences, 2014 - Elsevier
Kernel learning is becoming an important research topic in the area of machine learning,
and it has wide applications in pattern recognition, computer vision, image and signal …
and it has wide applications in pattern recognition, computer vision, image and signal …
Quasiconformal kernel common locality discriminant analysis with application to breast cancer diagnosis
JB Li, Y Peng, D Liu - Information Sciences, 2013 - Elsevier
Dimensionality reduction (DR) is a popular method in recognition and classification in many
areas, such as facial and medical imaging. In this paper, we propose a novel supervised DR …
areas, such as facial and medical imaging. In this paper, we propose a novel supervised DR …
Single-view RGBD-based reconstruction of dynamic human geometry
We present a method for reconstructing the geometry and appearance of indoor scenes
containing dynamic human subjects using a single (optionally moving) RGBD sensor. We …
containing dynamic human subjects using a single (optionally moving) RGBD sensor. We …