Impact of Data Preprocessing on Integrative Matrix Factorization of Single Cell Data
Frontiers in Oncology, 2020 Integrative, single-cell analyses may provide unprecedented insights into cellular and spatial diversity of the tumor microenvironment. The sparsity, noise, and high dimensionality of these data present unique challenges.
Lauren L. Hsu +3 more
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Data Reduction in Proportional Hazards Models Applied to Reliability Prediction of Centrifugal Pumps
MachinesThis paper presents the use of proportional hazards regression models for predicting the Mean Time Between Failures (MTBF) of centrifugal pumps in the oil and gas industry.
Marc Vila Forteza +3 more
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A Convex Sparse PCA for Feature Analysis
CoRR, 2014 Principal component analysis (PCA) has been widely applied to dimensionality reduction and data pre-processing for different applications in engineering, biology and social science. Classical PCA and its variants seek for linear projections of the original variables to obtain a low dimensional feature representation with maximal variance.
Xiaojun Chang +3 more
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Sparse PCA via matrix (2,1)-norm regularization with an application to feature selection
Results in Applied MathematicsThis paper is concerned with sparse PCA via the matrix (2,1)-norm regularization (PCA2,1). It can produce a row-sparse projection, a useful tool in machine learning when it comes to, for example, feature selection, that aims to choose most relevant ...
Li Wang, Jiawei Wang, Ren-Cang Li
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GLOBAL AND LOCAL SPARSE SUBSPACE OPTIMIZATION FOR MOTION SEGMENTATION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model. Since the feature trajectories in practice are high-dimensional and contain a lot of noise, we firstly apply the sparse PCA to represent ...
M. Ying Yang +3 more
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Image Compressive Sensing Reconstruction Based on z-Score Standardized Group Sparse Representation
IEEE Access, 2019 Non-local similarity-based group sparse representation (GSR) has shown great potential in image restoration. Considering the universal existing non-stationarity of natural images and the statistic characteristic differences of different components in the
Zhirong Gao +4 more
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Fast Semi-Supervised Unmixing of Hyperspectral Image by Mutual Coherence Reduction and Recursive PCA
Remote Sensing, 2018 Dictionary pruning step is often employed prior to the sparse unmixing process to improve the performance of library aided unmixing. This paper presents a novel recursive PCA approach for dictionary pruning of linearly mixed hyperspectral data motivated ...
Samiran Das +2 more
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Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification
AlgorithmsThe kinematics of the human hand exhibit complex and diverse characteristics unique to each individual. Various techniques such as vision-based, ultrasonic-based, and data-glove-based approaches have been employed to analyze human hand movements. However,
Jirayu Samkunta +5 more
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A D.C. Programming Approach to the Sparse Generalized Eigenvalue Problem
, 2009 In this paper, we consider the sparse eigenvalue problem wherein the goal is to obtain a sparse solution to the generalized eigenvalue problem. We achieve this by constraining the cardinality of the solution to the generalized eigenvalue problem and ...
Lanckriet, Gert +2 more
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The All-or-Nothing Phenomenon in Sparse Tensor PCA
CoRR, 2020 Corrected a typo in the statement of Theorem ...
Jonathan Niles-Weed, Ilias Zadik
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