Dimensionality Reduction for Partial Label Learning: A Unified and Adaptive Approach

Abstract

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 called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partial label Dimensionality Reduction via Adaptive Weight (</i><sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Draw</small><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">)</i> 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.