Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection
Prototype-based methods are of the particular interest for domain specialists and practitioners as they summarize a dataset by a small set of representatives. Therefore, in a classification setting, interpretability of the prototypes is as significant as the prediction accuracy of the algorithm. Nevertheless, the state-of-the-art methods make inefficient trade-offs between these concerns by sacrificing one in favor of the other, especially if the given data has a kernel-based representation. In this paper, we propose a novel interpretable multiple-kernel prototype learning (IMKPL) to construct highly interpretable prototypes in the feature space, which are also efficient for the discriminative representation of the data. Our method focuses on the local discrimination of the classes in the feature space and shaping the prototypes based on condensed class-homogeneous neighborhoods of data. Besides, IMKPL learns a combined embedding in the feature space in which the above objectives are better fulfilled. When the base kernels coincide with the data dimensions, this embedding results in a discriminative features selection. We evaluate IMKPL on several benchmarks from different domains which demonstrate its superiority to the related state-of-the-art methods regarding both interpretability and discriminative representation.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning
Multiple kernel learning (MKL) algorithms combine different base kernels to obtain a more efficient representation in the feature space. Focusing on discriminative tasks, MKL has been used successfully for feature select…
Binary Classificationfeature selectionGeneral ClassificationRepresentation LearningInterpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes
The task of graph-level anomaly detection (GLAD) is to identify anomalous graphs that deviate significantly from the majority of graphs in a dataset. While deep GLAD methods have shown promising performance, their black-…
Anomaly DetectionPrototype-Guided Saliency Feature Learning for Person Search
Existing person search methods integrate person detection and re-identification (re-ID) module into a unified system. Though promising results have been achieved, the misalignment problem, which commonly occurs in pe…
Human DetectionPerson SearchBeyond Patches: Mining Interpretable Part-Prototypes for Explainable AI
Deep learning has provided considerable advancements for multimedia systems, yet the interpretability of deep models remains a challenge. State-of-the-art post-hoc explainability methods, such as GradCAM, provide visual …
Unsupervised Part DiscoveryA Deep Positive-Negative Prototype Approach to Integrated Prototypical Discriminative Learning
This paper proposes a novel Deep Positive-Negative Prototype (DPNP) model that combines prototype-based learning (PbL) with discriminative methods to improve class compactness and separability in deep neural networks. Wh…