Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection

Chun, Hyonho; Keles, Suenduez

doi:10.1111/j.1467-9868.2009.00723.x

Cited by 723 publications

(709 citation statements)

References 39 publications

Supporting

Mentioning

695

Contrasting

Unclassified

Order By: Relevance

“…The model obtained is complex since new variables are represented in a new dimensional space [37]. The SPLS-DA is based in the sparsity principle [38], on the basis that not all explanatory variables are needed to the fit model, i.e., meaning that a subset of original variables could be mainly responsible for determining the response. Therefore, SPLS-DA also has the ability to perform feature selection keeping the structure of PLS-DA model.…”

Section: Discussionmentioning

confidence: 99%

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Dias

Rodrigues

Veloso

et al. 2015

Eur Food Res Technol

View full text Add to dashboard Cite

sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

show abstract

Section: Discussionmentioning

confidence: 99%

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Dias

Rodrigues

Veloso

et al. 2015

Eur Food Res Technol

View full text Add to dashboard Cite

show abstract

“…However, using sparse PLSR (Chun and Keles, 2010) instead of conventional PLSR could remedy this. Moreover, given the current state of the hardware this is not a problem for off-line analysis and model improvements.…”

Section: Resultsmentioning

confidence: 99%

Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

Gale¹,

Rahmati²,

Gravdahl³

et al. 2017

Front. Robot. AI

View full text Add to dashboard Cite

A new method is presented for extending a dynamic model of a six degrees of freedom robotic manipulator. A non-linear multivariate calibration of input-output training data from several typical motion trajectories is carried out with the aim of predicting the model systematic output error at time (t + 1) from known input reference up till and including time (t). A new partial least squares regression (PLSR) based method, nominal PLSR with interactions was developed and used to handle, unmodelled non-linearities. The performance of the new method is compared with least squares (LS). Different crossvalidation schemes were compared in order to assess the sampling of the state space based on conventional trajectories. The method developed in the paper can be used as fault monitoring mechanism and early warning system for sensor failure. The results show that the suggested methods improves trajectory tracking performance of the robotic manipulator by extending the initial dynamic model of the manipulator.

show abstract

“…Through a large, pre-determined number of repeated samplings with replacements, the distribution parameters of coefficients were estimated, and their confidence intervals (CI) were calculated. Features that did not pass the significance test were removed [46].…”

Section: Resultsmentioning

confidence: 99%

Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis

et al. 2017

View full text Add to dashboard Cite

Land cover information extraction through object-based image analysis (OBIA) has become an important trend in remote sensing, thanks to the increasing availability of high-resolution imagery. Segmented objects have a large number of features that cause high-dimension and low-sample size problems in the classification process. In this study, on the basis of a partial least squares generalized linear regression (PLSGLR), we propose a group corrected PLSGLR, known as G-PLSGLR, that aims to reduce the redundancy of object features for land cover identifications. Using Gaofen-2 images, the area of interest was segmented and sampled to generate small sample-size training datasets with 51 object features. The features selected by G-PLSGLR were compared against a guided regularized random forest (GRRF) in metrics of reduction rate, feature redundancy, and accuracy assessment of classification. Three indicators of overall accuracy (OA), user's accuracy (UA), and producer's accuracy (PA) were applied for accuracy assessment in this paper. The result shows that the G-PLSGLR achieved a reduction rate of 9.27 with a feature redundancy of 0.29, and a value of OA 90.63%. The GRRF achieved a reduction rate of 1.61 with a feature redundancy of 0.42, and a value of OA 85.56%. The PA of each land cover category was more than 95% using features selected by G-PLSGLR, while the PA ranged from 77 to 96% using features selected by GRRF. The UA of G-PLSGLR-selected features ranged from 70 to 80% except for grass land and bare land, which achieved 10% higher UA than GRRF-selected features. The G-PLSGLR method we proposed has the advantages of a large reduction rate, low feature redundancy, and high classification performance, which can be applied in OBIA-based land cover classification.

show abstract

Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection

Cited by 723 publications

References 39 publications

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis

Contact Info

Product

Resources

About