Label distribution learning (LDL) employs probabilistic labels to capture the varying degrees of relevance among decision attributes. Existing LDL algorithms usually employ sample correlations to improve their predictive validity. However, they merely employ the superficial features of the sample for correlation analysis, seldom delving into its latent deep features. In this paper, we propose an algorithm to explore the sample topology (ST-LDL) to address this problem. First, we construct a locally weighted directed graph for each target sample. The target sample and its k neighbors are regarded as nodes within the graph. The asymmetrical correlation among these $k+1$ samples is computed separately to determine the weight value of the graph. Then, we utilise the local topology between samples to mine latent information and reconstruct the samples. The local graph is fed into the graph convolutional network to nonlinearly reconstruct the features by mining the latent information. Finally, we designed a new optimization objective function for the reconstructed samples. Experiments are carried out on elven real-world datasets in comparison with seven state-of-the-art algorithms. The results show that our algorithm outperforms several other algorithms, proving the effectiveness of our algorithm.

Player behavior modeling is of the utmost importance in game development and player matching. This problem is challenging because behavior is multi-semantic and hard to represent. Existing work often suffers from low generalization ability and high demand for supervisory information. In this paper, we present a behavior representation learning method (BRL) for Reversi players. It learns entirely from unlabeled game records. First, we develop an asymmetric encoder-decoder architecture to learn the mapping between states and actions. The encoder maps game records into a latent subspace for behavior representation. Second, we mask random states to force the encoder to capture the high-level features of the policy. The decoder predicts the corresponding actions according to the latent representation. Coupling these two designs, the semantic relevance of behaviors can be more effectively measured. Experiments were conducted in Reversi, using 14,000 game records of different players to learn behavior representations. Transfer performance in downstream tasks outperforms the supervised method and shows promising scaling ability. This work opens a new way for analyzing and modeling player behavior.

Label distribution learning (LDL) can more accurately represent the degree of correlation between labels and samples than multi-label learning. However, LDL usually has limited available label data, which is not conducive to training deep learning models. Data augmentation refers to methods for solving limited data problems by introducing unobserved data or latent variables to increase the size and quality of the training dataset. In this paper, we augment the dataset by mapping the features and label distributions of the generated samples to the same subspace, and using the generator to learn the distribution of the original data in this space. First, we use the encoder and generator to extract effective information from sample features and label distributions, respectively. Second, we randomly fuse the existing label distribution to generate a new label distribution, then map it to the subspace and restore the corresponding features through the decoder. Finally, these generated samples are mixed with the original training set to train a model for predicting label distribution. Experimental results on nine real-world datasets show that our proposed algorithm can improve the performance of deep learning models to a certain extent.