Results 11 to 20 of about 564,038 (269)
Sparse Principal Component Analysis [PDF]
Journal of Computational and Graphical Statistics, 2006 Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results.
Hui Zou +2 more
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Sparse Principal Component Analysis via Variable Projection [PDF]
SIAM Journal on Applied Mathematics, 2020 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 between distinct time scales.
N. Benjamin Erichson +5 more
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Sparse Non-Gaussian Component Analysis [PDF]
IEEE Transactions on Information Theory, 2010 Non-gaussian component analysis (NGCA) introduced in offered a method for high dimensional data analysis allowing for identifying a low-dimensional non-Gaussian component of the whole distribution in an iterative and structure adaptive way. An important step of the NGCA procedure is identification of the non-Gaussian subspace using Principle Component ...
Diederichs, Elmar +3 more
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Rolling Bearing Fault Diagnosis Based on Nonlinear Underdetermined Blind Source Separation
Machines, 2022 One challenge of bearing fault diagnosis is that the vibration signals are often a nonlinear mixture of unknown source signals. In addition, the practical installation position also limits the number of observed signals. Hence, bearing fault diagnosis is
Hong Zhong +4 more
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Sparse Generalised Principal Component Analysis [PDF]
Pattern Recognition, 2018 In this paper, we develop a sparse method for unsupervised dimension reduction for data from an exponential-family distribution. Our idea extends previous work on Generalised Principal Component Analysis by adding L1 and SCAD penalties to introduce sparsity.
Luke Smallman +2 more
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Sparse exponential family Principal Component Analysis [PDF]
Pattern Recognition, 2016 We propose a Sparse exponential family Principal Component Analysis (SePCA) method suitable for any type of data following exponential family distributions, to achieve simultaneous dimension reduction and variable selection for better interpretation of the results. Because of the generality of exponential family distributions, the method can be applied
Lu, Meng +2 more
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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
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Stochastic convex sparse principal component analysis [PDF]
EURASIP Journal on Bioinformatics and Systems Biology, 2016 Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present in the data.
Baytas, Inci M. +4 more
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Face Recognition Based on Sparse Two-Direction Two-Dimensional Principle Component Analysis [PDF]
Jisuanji gongcheng, 2019 Two-Direction Two-Dimensional Principle Component Analysis((2D)2PCA) is an improved method of Principle Component Analysis(PCA) in the two-dimensional space.However,just like PCA,the (2D)2PCA is susceptible to abnormal values,its robustness is weak and ...
ZHANG Yuping, GONG Xiaofeng, LUO Ruisen
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JEDi: java essential dynamics inspector — a molecular trajectory analysis toolkit
BMC Bioinformatics, 2021 Background Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to
Charles C. David +2 more
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