Partial label learning induces classifier from data with ambiguous supervision, where each instance is associated with a set of candidate labels but only one of which is valid. As a classic data preprocessing strategy, dimensionality reduction contributes to enhance the generalization capabilities of learning algorithms. Due to the ambiguity of supervision, existing works on partial label dimensionality reduction are confined to two separate stages: dimensionality reduction and partial label disambiguation. However, the decoupling of dimensionality reduction from partial label disambiguation can lead to severe performance degradation. In this paper, we present a novel approach called Wrapped Partial Label Dimensionality Reduction (WPLDR) to address this challenge. Specifically, WPLDR integrates the dimensionality reduction and partial label disambiguation within a unified framework, employing alternating optimization to concurrently perform dimensionality reduction and partial label disambiguation. WPLDR maximizes the interdependence between features in the embedded space and confidence-based label information, while simultaneously ensuring the manifold consistency between the embedded feature space and label space. Extensive experiments over a broad range of synthetic and real-world partial label data sets validate that the performance of well-established partial label learning algorithms can be significantly improved by the proposed WPLDR.

Partial label learning learns from instances with weak supervision, where each instance is associated with a set of candidate labels, among which only one is valid. Recently, dimensionality reduction has emerged as an effective preprocessing strategy to improve generalization performance. Existing approaches mainly tackle this problem through supervised or unsupervised dimensionality reduction. However, the former requires ground-truth labels, which are concealed in candidate label sets. Consequently, methods in this line may suffer from overfitting due to false positive labels in candidate label set. Conversely, the latter overlooks weakly supervised information in training instances, leading to performance degradation. In this paper, we propose an approach calledpartial label Dimensionality Reduction via Adaptive Weight (Draw)to leverage the strengths of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Specifically, our approach tends to exploit unsupervised and data-driven nature of PCA to capture underlying structure of instances in initial stage. As the ground-truth label is gradually identified, our method increasingly relies on the discriminative ability of LDA to enhance the separation between different classes. Through extensive experiments on diverse partial label datasets, we validate that the proposed dimensionality reduction approach significantly improves classification performance of well-established partial label learning algorithms.