Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning

Learning explicit and implicit patterns in human trajectories plays an important role in many Location-Based Social Networks (LB-SNs) applications, such as trajectory classification (e.g., walking, driving, etc.), trajectory-user linking, friend recommendation, etc. A particular problem that has attracted much attention recentlyand is the focus of our work-is the Trajectory-based Social Circle Inference (TSCI), aiming at inferring user social circles (mainly social friendship) based on motion trajectories and without any explicit social networked information. Existing approaches addressing TSCI lack satisfactory results due to the challenges related to data sparsity, accessibility and model efficiency. Motivated by the recent success of machine learning in trajectory mining, in this paper we formulate TSCI as a novel multi-label classification problem and develop a Recurrent Neural Network (RNN)-based framework called DeepTSCI to use human mobility patterns for inferring corresponding social circles. We propose three methods to learn the latent representations of trajectories, based on: (1) bidirectional Long Short-Term Memory (LSTM); (2) Autoencoder; and (3) Variational autoencoder. Experiments conducted on real-world datasets demonstrate that our proposed methods perform well and achieve significant improvement in terms of macro-R, macro-F1 and accuracy when compared to baselines.

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A multi-instance multi-label learning algorithm based on radial basis functions and multi-objective particle swarm optimization

Bao,

Han,

Ling

et al. 2023

IDA

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Radial basis function (RBF) neural networks for Multi-Instance Multi-Label (MIML) directly can exploit the connections between instances and labels so that they can preserve useful prior information, but they only adopt Gaussian radial basis function as their RBF whose parameters are difficult to determine. In this paper, parameters can be obtained by multi-objective optimization methods with multi performance measures treated as objectives, specifically, parameter estimation of different RBFs by an improved multi-objective particle swarm optimization (MOPSO) is proposed where Recall rate and Precision rate are chosen to obtain the most desirable Pareto optimal solution set. Furthermore, share-learning factor is proposed to modify the particle velocity in standard MOPSO to improve the global search ability and group cooperative ability. It is experimentally demonstrated that the proposed method can estimate the reliable parameters of different RBFs, and it is also very competitive with the state of art MIML methods.

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Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning

Cited by 3 publications

References 18 publications

Skew-Robust Human-Object Interactions in Videos

Skew-Robust Human-Object Interactions in Videos

Trajectory-based social circle inference

A multi-instance multi-label learning algorithm based on radial basis functions and multi-objective particle swarm optimization

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