Results 41 to 50 of about 25,940 (257)
Sparse PCA via Bipartite Matchings
CoRR, 2015 We consider the following multi-component sparse PCA problem: given a set of data points, we seek to extract a small number of sparse components with disjoint supports that jointly capture the maximum possible variance. These components can be computed one by one, repeatedly solving the single-component problem and deflating the input data matrix, but ...
Megasthenis Asteris +3 more
openaire +3 more sources
Sparse PCA Beyond Covariance Thresholding
CoRR, 2023 In the Wishart model for sparse PCA we are given $n$ samples $Y_1,\ldots, Y_n$ drawn independently from a $d$-dimensional Gaussian distribution $N({0, Id + βvv^\top})$, where $β> 0$ and $v\in \mathbb{R}^d$ is a $k$-sparse unit vector, and we wish to recover $v$ (up to sign).
openaire +3 more sources
Class-Specific Sparse Principal Component Analysis for Visual Classification
IEEE Access, 2020 Extensive research has demonstrated that dictionary learning is active in improving the performance of the representation based classification. However, dictionary learning suffers from lacking an effective dictionary structure that can well tradeoff the
Fei Pan +3 more
doaj +1 more source
SUPER-RESOLUTION OF HYPERSPECTRAL IMAGES USING COMPRESSIVE SENSING BASED APPROACH [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 Over the past decade hyper spectral (HS) image analysis has turned into one of the most powerful and growing technologies in the field of remote sensing.
R. C. Patel, M. V. Joshi
doaj +1 more source
On the Worst-Case Approximability of Sparse PCA
CoRR, 2015 20 ...
Siu On Chan +2 more
openaire +2 more sources
Sparse Kernel PCA for Outlier Detection
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018 Accepted at IEEE ICMLA 2018 for Oral ...
Rudrajit Das +2 more
openaire +2 more sources
FEBS Open Bio, EarlyView.This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill +4 more
wiley +1 more source
Incremental Sparse Adaptive PCA for Streaming Industrial Sensor Data
TelecomIndustrial Internet of Things (IIoT) systems generate high-dimensional, non-stationary sensor streams under strict memory and computational constraints, limiting the applicability of classical batch dimensionality reduction methods. While incremental PCA
Rebin Saleh, Balázs Villányi
doaj +1 more source
IEEE Journal of Translational Engineering in Health and Medicine, 2019 Background: EEG signals are extremely complex in comparison to other biomedical signals, thus require an efficient feature selection as well as classification approach. Traditional feature extraction and classification methods require to reshape the data
Imran Razzak +2 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed +6 more
wiley +1 more source