Noisy multi-label semi-supervised dimensionality reduction

Abstract

Noisy labeled data represent a rich source of information that often are easily accessible and cheap to obtain, but label noise might also have many negative consequences if not accounted for. How to fully utilize noisy labels has been studied extensively within the framework of standard supervised machine learning over a period of several decades. However, very little research has been conducted on solving the challenge posed by noisy labels in non-standard settings. This includes situations where only a fraction of the samples are labeled (semi-supervised) and each high-dimensional sample is associated with multiple labels. In this work, we present a novel semi-supervised and multi-label dimensionality reduction method that effectively utilizes information from both noisy multi-labels and unlabeled data. With the proposed Noisy multi-label semi-supervised dimensionality reduction (NMLSDR) method, the noisy multi-labels are denoised and unlabeled data are labeled simultaneously via a specially designed label propagation algorithm. NMLSDR then learns a projection matrix for reducing the dimensionality by maximizing the dependence between the enlarged and denoised multi-label space and the features in the projected space. Extensive experiments on synthetic data, benchmark datasets, as well as a real-world case study, demonstrate the effectiveness of the proposed algorithm and show that it outperforms state-of-the-art multi-label feature extraction algorithms.

38 pages

References in corpus (6)

• Discrete Signal Processing on Graphs
• Classification with Noisy Labels by Importance Reweighting
• High-Breakdown Robust Multivariate Methods
• Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks
• Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification
• Weighting Scheme for a Pairwise Multi-label Classifier Based on the Fuzzy Confusion Matrix

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