Results 11 to 20 of about 490,920 (259)
Incremental Canonical Correlation Analysis
Applied Sciences, 2020 Canonical correlation analysis (CCA) is a kind of a simple yet effective multiview feature learning technique. In general, it learns separate subspaces for two views by maximizing their correlations.
Hongmin Zhao, Dongting Sun, Zhigang Luo
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Canonical Correlation Analysis to Biomass CHONS Prediction
Chemical Engineering Transactions, 2023 Fermentation biomasses can be defined as a complex mixture of different natural components and microbes, having biodegradable and organic waste as the primary source. Its correct characterization is crucial to have proper processing in fermentative units.
Federico Moretta +4 more
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Neurons as Canonical Correlation Analyzers
Frontiers in Computational Neuroscience, 2020 Normative models of neural computation offer simplified yet lucid mathematical descriptions of murky biological phenomena. Previously, online Principal Component Analysis (PCA) was used to model a network of single-compartment neurons accounting for ...
Cengiz Pehlevan +4 more
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Canonical Correlation between Fathering and Emotional Autonomy [PDF]
Faṣlnāmah-i Farhang Mushavirah va Ravān/Darmānī, 2015 از سال 1986 که استینبرگ و سیلوربرگ برای اولینبار اصطلاح استقلال عاطفی را برای جدایی عاطفی نوجوان از والدینشان به کار بردند؛ در رابطه با عوامل تأثیرگذار بر آن، پژوهشهای زیادی صورت گرفت.
asieh omidvar tehrani +1 more
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Nonlinear Canonical Correlation Analysis:A Compressed Representation Approach
Entropy, 2020 Canonical Correlation Analysis (CCA) is a linear representation learning method that seeks maximally correlated variables in multi-view data. Nonlinear CCA extends this notion to a broader family of transformations, which are more powerful in many real ...
Amichai Painsky +2 more
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Process Monitoring Using Canonical Correlation Analysis
JISR on Computing, 2019 Principal component analysis (PCA) and partial least square (PLS) used for fault diagnosis and process monitoring for systems. It is expected that the information to be examined isn't self-connected.
Yin SHEN +4 more
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Kernel functional canonical correlation analysis
Acta Universitatis Lodziensis. Folia Oeconomica, 2016 Canonical correlation methods for data representing functions or curves have received much attention in recent years. Such data, known in the literature as functional data (Ramsay and Silverman, 2005), has been the subject of much recent research ...
Mirosław Krzyśko, Łukasz Waszak
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Determinant indicators for labor market efficiency and higher education and training: evidence from Middle East and North Africa countries [PDF]
Problems and Perspectives in Management, 2020 This study aims to explore the determinant indicators for the labor market efficiency and the higher education and training factors that can help in increasing the productivity in labor market and the quality in higher education and training, as well as ...
Elsayed A. H. Elamir
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Functional data analysis: Application to daily observation of COVID-19 prevalence in France
AIMS Mathematics, 2022 In this paper we use the technique of functional data analysis to model daily hospitalized, deceased, Intensive Care Unit (ICU) cases and return home patient numbers along the COVID-19 outbreak, considered as functional data across different departments ...
Kayode Oshinubi +3 more
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Sufficient Canonical Correlation Analysis [PDF]
IEEE Transactions on Image Processing, 2016 Canonical correlation analysis (CCA) is an effective way to find two appropriate subspaces in which Pearson's correlation coefficients are maximized between projected random vectors. Due to its well-established theoretical support and relatively efficient computation, CCA is widely used as a joint dimension reduction tool and has been successfully ...
Guo, Y, Ding, X, Liu, C, Xue, J-H
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