Low-dimensional Interpretable Kernels with Conic Discriminant Functions for Classification
Kernels are often developed and used as implicit mapping functions that show impressive predictive power due to their high-dimensional feature space representations. In this study, we gradually construct a series of simple feature maps that lead to a collection of interpretable low-dimensional kernels. At each step, we keep the original features and make sure that the increase in the dimension of input data is extremely low, so that the resulting discriminant functions remain interpretable and amenable to fast training. Despite our persistence on interpretability, we obtain high accuracy results even without in-depth hyperparameter tuning. Comparison of our results against several well-known kernels on benchmark datasets show that the proposed kernels are competitive in terms of prediction accuracy, while the training times are significantly lower than those obtained with state-of-the-art kernel implementations.
Code (1)
Tasks
ClassificationGeneral ClassificationSimilar Papers 제목 키워드 기반
Polyhedral Conic Classifiers for Visual Object Detection and Classification
We propose a family of quasi-linear discriminants that outperform current large-margin methods in sliding window visual object detection and open set recognition tasks. In these tasks the classification problems are b…
ClassificationGeneral ClassificationObjectobject-detection+2High-Dimensional Regularized Discriminant Analysis
Regularized discriminant analysis (RDA), proposed by Friedman (1989), is a widely popular classifier that lacks interpretability and is impractical for high-dimensional data sets. Here, we present an interpretable and co…
General ClassificationVocal Bursts Intensity PredictionLearning Interpretable Characteristic Kernels via Decision Forests
Decision forests are widely used for classification and regression tasks. A lesser known property of tree-based methods is that one can construct a proximity matrix from the tree(s), and these proximity matrices are indu…
Feature ImportanceGeneral ClassificationTransfer Representation Learning with TSK Fuzzy System
Transfer learning can address the learning tasks of unlabeled data in the target domain by leveraging plenty of labeled data from a different but related source domain. A core issue in transfer learning is to learn a sha…
Dimensionality ReductionRepresentation LearningTransfer LearningDiagonal Discriminant Analysis with Feature Selection for High Dimensional Data
We introduce a new method of performing high dimensional discriminant analysis, which we call multiDA. We achieve this by constructing a hybrid model that seamlessly integrates a multiclass diagonal discriminant analysis…
feature selectionGeneral ClassificationTwo-sample testingVocal Bursts Intensity Prediction