scholarly journals Functional Principal Component Analysis: A Robust Method for Time-Series Phenotypic Data

2020 ◽  
Vol 183 (4) ◽  
pp. 1422-1423
Author(s):  
Yunqing Yu
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Robust Method ◽  
Phenotypic Data ◽  
Functional Principal Component

Robust Kernel Principal Component Analysis

2009 ◽  
Vol 21 (11) ◽  
pp. 3179-3213 ◽  
Author(s):  
Su-Yun Huang ◽  
Yi-Ren Yeh ◽  
Shinto Eguchi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Eigenvalue Decomposition ◽  
Robust Method ◽  
Data Contamination ◽  
Functional Principal Component ◽  
Robust Procedures

This letter discusses the robustness issue of kernel principal component analysis. A class of new robust procedures is proposed based on eigenvalue decomposition of weighted covariance. The proposed procedures will place less weight on deviant patterns and thus be more resistant to data contamination and model deviation. Theoretical influence functions are derived, and numerical examples are presented as well. Both theoretical and numerical results indicate that the proposed robust method outperforms the conventional approach in the sense of being less sensitive to outliers. Our robust method and results also apply to functional principal component analysis.

scholarly journals Mapping Mediterranean Forest Plant Associations and Habitats with Functional Principal Component Analysis Using Landsat 8 NDVI Time Series

2020 ◽  
Vol 12 (7) ◽  
pp. 1132 ◽  
Author(s):  
Simone Pesaresi ◽  
Adriano Mancini ◽  
Giacomo Quattrini ◽  
Simona Casavecchia
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Mediterranean Forest ◽  
Plant Associations ◽  
Functional Principal Component ◽  
Ndvi Time Series

The classification of plant associations and their mapping play a key role in defining habitat biodiversity management, monitoring, and conservation strategies. In this work we present a methodological framework to map Mediterranean forest plant associations and habitats that relies on the application of the Functional Principal Component Analysis (FPCA) to the remotely sensed Normalized Difference Vegetation Index (NDVI) time series. FPCA, considering the chronological order of the data, reduced the NDVI time series data complexity and provided (as FPCA scores) the main seasonal NDVI phenological variations of the forests. We performed a supervised classification of the FPCA scores combined with topographic and lithological features of the study area to map the forest plant associations. The supervised mapping achieved an overall accuracy of 87.5%. The FPCA scores contributed to the global accuracy of the map much more than the topographic and lithological features. The results showed that (i) the main seasonal phenological variations (FPCA scores) are effective spatial predictors to obtain accurate plant associations and habitat maps; (ii) the FPCA is a suitable solution to simultaneously express the relationships between remotely sensed and ecological field data, since it allows us to integrate these two different perspectives about plant associations in a single graph. The proposed approach based on the FPCA is useful for forest habitat monitoring, as it can contribute to produce periodically detailed vegetation-based habitat maps that reflect the “current” status of vegetation and habitats, also supporting the study of plant associations.

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