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Bjarne K. Ersbøll

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EAAI Journal 2017 Journal Article

Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

  • Sara Sharifzadeh
  • Ali Ghodsi
  • Line H. Clemmensen
  • Bjarne K. Ersbøll

Principal component analysis (PCA) 1 1 PCA: principal component analysis, SPCA: sparse PCA, SSPCA: sparse supervised PCA, SPLS: sparse partial least squares, PMD: penalized matrix decomposition, SVD: singular value decomposition, HSIC: Hilbert Schmidt independence criterion, RKHS: reproducing kernel Hilbert space, SIMPLS: statistically inspired modification of PLS, SVM: support vector machine, CV: cross validation, RBF: radial basis function, RMSE: root mean square error, ROI: region of interest, NIR: near infrared, SSC: solvable solid content, KNN: K nearest neighbour. is one of the main unsupervised pre-processing methods for dimension reduction. When the training labels are available, it is worth using a supervised PCA strategy. In cases that both dimension reduction and variable selection are required, sparse PCA (SPCA) methods are preferred. In this paper, a sparse supervised PCA (SSPCA) method is proposed for pre-processing. This method is appropriate especially in problems where, a high dimensional input necessitates the use of a sparse method and a target label is also available to guide the variable selection strategy. Such a method is valuable in many Engineering and scientific problems, when the number of training samples is also limited. The Hilbert Schmidt Independence Criteria (HSIC) is used to form an objective based on minimization of a loss function and an L 1 norm is used for regularization of the Eigen vectors. While the proposed objective function allows a sparse low rank solution for both linear and non-linear relationships between the input and response matrices, other similar methods in this case are only based on a linear model. The objective is solved based on penalized matrix decomposition (PMD) algorithm. We compare the proposed method with PCA, PMD-based SPCA and supervised PCA. In addition, SSPCA is also compared with sparse partial least squares (SPLS), due to the similarity between the two objective functions. Experimental results from the simulated as well as real data sets show that, SSPCA provides an appropriate trade-off between accuracy and sparsity. Comparisons show that, in terms of sparsity, SSPCA performs the highest level of variable reduction and also, in terms of accuracy it is one of the most successful methods. Therefore, the Eigen vectors found by SSPCA can be used for feature selection in various high dimensional problems.

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EAAI Journal 2014 Journal Article

Supervised feature selection for linear and non-linear regression of L⁎a⁎b⁎ color from multispectral images of meat

  • Sara Sharifzadeh
  • Line H. Clemmensen
  • Claus Borggaard
  • Susanne Støier
  • Bjarne K. Ersbøll

In food quality monitoring, color is an important indicator factor of quality. The CIELab (L⁎a⁎b⁎) color space as a device independent color space is an appropriate means in this case. The commonly used colorimeter instruments can neither measure the L⁎a⁎b color in a wide area over the target surface nor in a contact-less mode. However, developing algorithms for conversion of food items images into L⁎a⁎b color space can solve both of these issues. This paper addresses the problem of L⁎a⁎b color prediction from multispectral images of different types of raw meat. The efficiency of using multispectral images instead of the standard RGB is investigated. In addition, it is demonstrated that due to the fiber structure and transparency of raw meat, the prediction models built on the standard color patches do not work for raw meat test samples. As a result, multispectral images of different types of meat samples (430–970nm) were used for training and testing of the L⁎a⁎b prediction models. Finding a sparse solution or the use of a minimum number of bands is of particular interest to make an industrial vision set-up simpler and cost effective. In this paper, a wide range of linear, non-linear, kernel-based regression and sparse regression methods are compared. In order to improve the prediction results of these models, we propose a supervised feature selection strategy which is compared with the Principal component analysis (PCA) as a pre-processing step. The results showed that the proposed feature selection method outperforms the PCA for both linear and non-linear methods. The highest performance was obtained by linear ridge regression applied on the selected features from the proposed Elastic net (EN) -based feature selection strategy. All the best models use a reduced number of wavelengths for each of the L⁎a⁎b components.

Details DOI
v2026.09.13