Supervised feature selection with orthogonal regression and feature weighting
Effective features can improve the performance of a model, which can thus help us understand the characteristics and underlying structure of complex data. Previous feature selection methods usually cannot keep more local structure information. To address the defects previously mentioned, we propose a novel supervised orthogonal least square regression model with feature weighting for feature selection. The optimization problem of the objection function can be solved by employing generalized power iteration (GPI) and augmented Lagrangian multiplier (ALM) methods. Experimental results show that the proposed method can more effectively reduce the feature dimensionality and obtain better classification results than traditional feature selection methods. The convergence of our iterative method is proved as well. Consequently, the effectiveness and superiority of the proposed method are verified both theoretically and experimentally.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionregressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unsupervised Simultaneous Orthogonal Basis Clustering Feature Selection
In this paper, we propose a novel unsupervised feature selection method: Simultaneous Orthogonal basis Clustering Feature Selection (SOCFS). To perform feature selection on unlabeled data effectively, a regularized regre…
Clusteringfeature selectionEmbedded Multi-label Feature Selection via Orthogonal Regression
In the last decade, embedded multi-label feature selection methods, incorporating the search for feature subsets into model optimization, have attracted considerable attention in accurately evaluating the importance of f…
feature selectionModel OptimizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Unsupervised feature selection using Bayesian Tucker decomposition
In this paper, we proposed Bayesian Tucker decomposition (BTuD) in which residual is supposed to obey Gaussian distribution analogous to linear regression. Although we have proposed an algorithm to perform the proposed B…
Feature Selection Based on Orthogonal Constraints and Polygon Area
The goal of feature selection is to choose the optimal subset of features for a recognition task by evaluating the importance of each feature, thereby achieving effective dimensionality reduction. Currently, proposed fea…
Dimensionality Reductionfeature selectionGrouped Orthogonal Matching Pursuit for Variable Selection and Prediction
We consider the problem of variable group selection for least squares regression, namely, that of selecting groups of variables for best regression performance, leveraging and adhering to a natural grouping structure wit…
feature selectionPredictionregressionVariable Selection