Results 91 to 100 of about 7,040 (158)

Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization. [PDF]

Sci Rep
Goel R, Bansal S, Gupta K.
europepmc   +1 more source

Feature level quantitative ultrasound and CT information fusion to predict the outcome of head & neck cancer radiotherapy treatment: Enhanced principal component analysis. [PDF]

Med Phys
Moslemi A, Safakish A, Sannachi L, Alberico D, Czarnota GJ.   +4 more
europepmc   +1 more source

Uncertainty-Aware Remaining Useful Life Prediction via Synergizing TCN-Transformer Networks and Fractional Brownian Motion. [PDF]

Entropy (Basel)
Geng Y, Yu T, Liu Y, Zhao J.
europepmc   +1 more source

Preliminary Analysis of Collar Sensors for Guide Dog Training Using Convolutional Long Short-Term Memory, Kernel Principal Component Analysis and Multi-Sensor Data Fusion. [PDF]

Animals (Basel)
Martin D, Roberts DL, Bozkurt A.
europepmc   +1 more source

Attention Mechanism-Based Feature Fusion and Degradation State Classification for Rolling Bearing Performance Assessment. [PDF]

Sensors (Basel)
Zhan T, Chen W, Xu C, Li L, Ding X.
europepmc   +1 more source

Lorentz-regularized interpretable VAE for multi-scale single-cell transcriptomic and epigenomic embeddings. [PDF]

Front Genet
Fu Z, Fu J, Chen C, Zhang K, Wang S.
europepmc   +1 more source

Rapid identification model of mine water inrush source using random forest optimized by multi-strategy improved sparrow search algorithm.

Heliyon
Ling J, Fu Z, Xue K.
europepmc   +1 more source

L1 norm based KPCA for novelty detection

Pattern Recognition, 2013
Novelty detection is a one class classification problem, and it builds up the model with only normal samples, based on which the novelty is detected. Though conventional KPCA is an effective method of building one class classification models, it is prone to being affected by the presence of outliers due to its inherent properties of L2 norm.
Huangang Wang, Junwu Zhou
exaly   +2 more sources

Face Recognition Using KPCA and KFDA

Applied Mechanics and Materials, 2013
KPCA extracting principal component with nonlinear method is an improved PCA. The KPCA can extract the feature set which is more suitable in categorization than the conventional PCA. The method of KFDA is equivalent to KPCA plus LDA. KPCA is first performed and then LDA is used for a second feature extraction in the KPCA-transformed space. The KPCA and
Hong Mei Li, Dong Ming Zhou, Ren Can Nie, Xiang Li, Hai Ying Deng   +4 more
exaly   +2 more sources

Image classification with parallel KPCA‐PCA network

Computational Intelligence, 2022
AbstractPrincipal component analysis (PCA) is widely used in computer vision for object detection. In this article, we take advantage of the algorithms of PCA and kernel principal component analysis (KPCA) to construct a deep learning model named parallel KPCA‐PCA network (PK‐PCANet).
Feng Yang, Zheng Ma 0005, Mei Xie
openaire   +1 more source