Robust principal component analysis
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Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm Open
Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly co…
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Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection Open
\n\tMany state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly heterogeneous backgrounds, they woul…
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FlashPCA2: principal component analysis of Biobank-scale genotype datasets Open
Motivation Principal component analysis (PCA) is a crucial step in quality control of genomic data and a common approach for understanding population genetic structure. With the advent of large genotyping studies involving hundreds of thou…
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Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery Open
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On the Applications of Robust PCA in Image and Video Processing Open
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Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids Open
This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. First, methods of constructing sparse stealth attacks are developed for two typical scenarios: 1) random attacks…
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Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering Open
Non-negative matrix factorization (NMF) has been one of the most popular methods for feature learning in the field of machine learning and computer vision. Most existing works directly apply NMF on high-dimensional image datasets for compu…
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Sparse Principal Component Analysis via Variable Projection Open
Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis, providing improved interpretation of low-rank structures by identifying localized spatial structures in the data and disambiguating be…
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Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis Open
This paper presents a remarkably simple, yet powerful, algorithm termed\nCoherence Pursuit (CoP) to robust Principal Component Analysis (PCA). As\ninliers lie in a low dimensional subspace and are mostly correlated, an inlier\nis likely to…
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Background–Foreground Modeling Based on Spatiotemporal Sparse Subspace Clustering Open
Background estimation and foreground segmentation are important steps in many high-level vision tasks. Many existing methods estimate background as a low-rank component and foreground as a sparse matrix without incorporating the structural…
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Link prediction via matrix completion Open
Inspired by practical importance of social networks, economic networks,\nbiological networks and so on, studies on large and complex networks have\nattracted a surge of attentions in the recent years. Link prediction is a\nfundamental issu…
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Innovative Hybrid Approach for Masked Face Recognition Using Pretrained Mask Detection and Segmentation, Robust PCA, and KNN Classifier Open
Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, and public safety. Masked face recognition is essential to accurately…
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Robust principal component analysis for accurate outlier sample detection in RNA-Seq data Open
Background High throughput RNA sequencing is a powerful approach to study gene expression. Due to the complex multiple-steps protocols in data acquisition, extreme deviation of a sample from samples of the same treatment group may occur du…
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Randomized Matrix Decompositions Using <i>R</i> Open
Matrix decompositions are fundamental tools in the area of applied\nmathematics, statistical computing, and machine learning. In particular,\nlow-rank matrix decompositions are vital, and widely used for data analysis,\ndimensionality redu…
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Convex Sparse PCA for Unsupervised Feature Learning Open
Principal component analysis (PCA) has been widely applied to dimensionality reduction and data pre-processing for different applications in engineering, biology, social science, and the like. Classical PCA and its variants seek for linear…
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Robust Principal Component Analysis on Graphs Open
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA sol…
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Pcadapt: An R Package to Perform Genome Scans for Selection Based on Principal Component Analysis Open
The R package pcadapt performs genome scans to detect genes under selection based on population genomic data. It assumes that candidate markers are outliers with respect to how they are related to population structure. Because population s…
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Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences Open
Data processing constitutes a critical component of high-contrast exoplanet\nimaging. Its role is almost as important as the choice of a coronagraph or a\nwavefront control system, and it is intertwined with the chosen observing\nstrategy.…
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Dual-Principal Component Analysis of the Raman Spectrum Matrix to Automatically Identify and Visualize Microplastics and Nanoplastics Open
As emerging contaminants, microplastics are challenging to characterize, particularly when their size is at the nanoscale. While imaging technology has received increasing attention recently, such as Raman imaging, decoding the scanning sp…
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EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network Open
The use of electroencephalography (EEG) has recently grown as a means to diagnose neurodegenerative pathologies such as Alzheimer’s disease (AD). AD recognition can benefit from machine learning methods that, compared with traditional manu…
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RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis Open
Key frame extraction algorithms consider the problem of selecting a subset of the most informative frames from a video to summarize its content. Several applications, such as video summarization, search, indexing, and prints from video, ca…
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Multiscale Superpixelwise Prophet Model for Noise-Robust Feature Extraction in Hyperspectral Images Open
Despite of various approaches proposed to smooth the hyperspectral images (HSIs) before feature extraction, the efficacy is still affected by the noise, even using the corrected dataset with the noisy and water absorption bands discarded. …
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Enhanced Tensor Low-Rank and Sparse Representation Recovery for Incomplete Multi-View Clustering Open
Incomplete multi-view clustering (IMVC) has attracted remarkable attention due to the emergence of multi-view data with missing views in real applications. Recent methods attempt to recover the missing information to address the IMVC probl…
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Scalable Robust Matrix Recovery: Frank--Wolfe Meets Proximal Methods Open
Recovering matrices from compressive and grossly corrupted observations is a\nfundamental problem in robust statistics, with rich applications in computer\nvision and machine learning. In theory, under certain conditions, this problem\ncan…
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Robust Multiple Model Fitting with Preference Analysis and Low-rank Approximation Open
This paper deals with the extraction of multiple models from outlier-contaminated data. The method we present is based on preference analysis and low rank approximation. After representing points in a conceptual space, Robust PCA (Principa…
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Double-constrained RPCA based on saliency maps for foreground detection in automated maritime surveillance Open
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Background Subtraction via Superpixel-Based Online Matrix Decomposition with Structured Foreground Constraints Open
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Infrared Small Target Detection via Spatial-Temporal Total Variation Regularization and Weighted Tensor Nuclear Norm Open
The infrared small and dim targets are often buried in strong clutters and noise, which requires robust and efficient detection approaches to achieve search and track task. In this paper, a novel infrared small target detection approach ba…
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A robust AMMI model for the analysis of genotype-by-environment data Open
Motivation: One of the most widely used models to analyse genotype-by-environment data is the additive main effects and multiplicative interaction (AMMI) model. Genotype-by-environment data resulting from multi-location trials are usually …
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Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation Open
Robust principal component analysis (RPCA) is a widely used tool for\ndimension reduction. In this work, we propose a novel non-convex algorithm,\ncoined Iterated Robust CUR (IRCUR), for solving RPCA problems, which\ndramatically improves …