Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold
Dimensionality reduction (DR) on the manifold includes effective methods which project the data from an implicit relational space onto a vectorial space. Regardless of the achievements in this area, these algorithms suffer from the lack of interpretation of the projection dimensions. Therefore, it is often difficult to explain the physical meaning behind the embedding dimensions. In this research, we propose the interpretable kernel DR algorithm (I-KDR) as a new algorithm which maps the data from the feature space to a lower dimensional space where the classes are more condensed with less overlapping. Besides, the algorithm creates the dimensions upon local contributions of the data samples, which makes it easier to interpret them by class labels. Additionally, we efficiently fuse the DR with feature selection task to select the most relevant features of the original space to the discriminative objective. Based on the empirical evidence, I-KDR provides better interpretations for embedding dimensions as well as higher discriminative performance in the embedded space compared to the state-of-the-art and popular DR algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
Dimensionality Reductionfeature selectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networks
Feature ranking and selection is a widely used approach in various applications of supervised dimensionality reduction in discriminative machine learning. Nevertheless there exists significant evidence on feature ranking…
Dimensionality Reductionfeature selectionSupervised dimensionality reductionSparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality Reduction
The most effective dimensionality reduction procedures produce interpretable features from the raw input space while also providing good performance for downstream supervised learning tasks. For many methods, this requir…
Dimensionality Reductionfeature selectionEffective Discriminative Feature Selection with Non-trivial Solutions
Feature selection and feature transformation, the two main ways to reduce dimensionality, are often presented separately. In this paper, a feature selection method is proposed by combining the popular transformation base…
Dimensionality Reductionfeature selectionFeature 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 selectionCovariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method
Dimensionality reduction plays an important role in computer vision problems since it reduces computational cost and is often capable of yielding more discriminative data representation. In this context, Partial Least Sq…
Computational EfficiencyDimensionality ReductionFace Verificationfeature selection+3