Results 81 to 90 of about 90,827 (166)

Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle

The Scientific World Journal, 2014
As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC) has attracted much attention in synthetic aperture radar (SAR) automatic target recognition (ATR) recently ...
Xiangwei Xing, Kefeng Ji, Huanxin Zou, Jixiang Sun   +3 more
doaj   +1 more source

Illumination and expression robust face recognition using collaboration of double-dictionary's sparse representation-based classification

Dianxin kexue, 2017
A face recognition method named WT-CSRC was proposed by using wavelet transform (WT) and a collaboration of double-dictionary's sparse representation-based classification (CSRC).
Fei GONG, Wei JIN, Keqing ZHU, Randi FU, Yan CAO   +4 more
doaj   +2 more sources

Super-sparse principal component analyses for high-throughput genomic data

BMC Bioinformatics, 2010
Background Principal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. However, it is often difficult to interpret the results because the principal components are linear combinations of all ...
Lee Youngjo, Lee Woojoo, Lee Donghwan, Pawitan Yudi   +3 more
doaj   +1 more source

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

Applied Sciences, 2017
In recent years, sparse representation approaches have been integrated into multi-focus image fusion methods. The fused images of sparse-representation-based image fusion methods show great performance.
Zhiqin Zhu, Guanqiu Qi, Yi Chai, Penghua Li   +3 more
doaj   +1 more source

Quartic Quantum Speedups for Planted Inference

Physical Review X
We describe a quantum algorithm for the Planted Noisy kXOR Problem (also known as Sparse Learning Parity with Noise) that achieves a nearly quartic (fourth-power) speedup over the best known classical algorithm while using exponentially less space.
Alexander Schmidhuber, Ryan O’Donnell, Robin Kothari, Ryan Babbush   +3 more
doaj   +1 more source

Ensemble Principal Component Analysis

IEEE Access
Efficient representations of data are essential for processing, exploration, and human understanding, and Principal Component Analysis (PCA) is one of the most common dimensionality reduction techniques used for the analysis of large, multivariate ...
Olga Dorabiala, Aleksandr Y. Aravkin, J. Nathan Kutz   +2 more
doaj   +1 more source

Factor analysis, sparse PCA, and Sum of Ranking Differences-based improvements of the Promethee-GAIA multicriteria decision support technique. [PDF]

PLoS One, 2022
Abonyi J, Czvetkó T, Kosztyán ZT, Héberger K.   +3 more
europepmc   +1 more source

Decomposing the Sri Lanka Yield Curve Using Principal Component Analysis to Examine the Term Structure of the Interest Rate

Engineering Proceedings
In this study, we delve into the dynamics of the Sri Lankan government bond market, building upon prior research that focused on the application of principal component analysis (PCA) in modelling sovereign yield curves.
K P N Sanjeewa Dayarathne, Uthayasanker Thayasivam   +1 more
doaj   +1 more source

Oja's Algorithm for Streaming Sparse PCA

Advances in Neural Information Processing Systems 37
Oja's algorithm for Streaming Principal Component Analysis (PCA) for $n$ data-points in a $d$ dimensional space achieves the same sin-squared error $O(r_{\mathsf{eff}}/n)$ as the offline algorithm in $O(d)$ space and $O(nd)$ time and a single pass through the datapoints.
Syamantak Kumar, Purnamrita Sarkar
openaire   +3 more sources

Kernel-Based Dimension Reduction Method for Time Series Nowcasting

IEEE Access
Nowcasts are closely related to big data. Currently, the most popular nowcast model-building approach is to use the factor bridge equation (BE) model or factor mixed data sampling (MIDAS) model, where the factors are extracted from large datasets using ...
Thanh Do Van, Hai Nguyen Minh
doaj   +1 more source