A New multi-instance multi-label learning approach for image and text classification

Recently, a reasonable and effectively framework to deal with the classification problem of the polysemy object with complex connotation is multi-instance multi-label (MIML) learning framework in which each example is not only represented by multiple instances but also associated with multiple labels. As we all know, feature expression plays an important role in the classification problems. It determines the accuracy of the classification results from the source. Considering its difficulties for automatically extracting the high-level features which are useful and noiseless for the MIML problem, so in this paper we present a general MIML framework by combining the feature learning technologies with machine learning technologies. Further, based on this framework, a new approach called CPNMIML which combines the probabilistic latent semantic analysis (PLSA) with the neural networks (NN) is proposed. In CPNMIML algorithm, we firstly learn the latent topic allocation of all the training examples by using the PLSA model, it is a feature learning process to get high-level features. Then we utilize the learned latent topic allocation of each training example to train the neural networks. Given a test example, we learn its latent topic distribution. Finally, we send the learned latent topic allocation of the test example to the trained neural networks to get the multiple labels of the test example. Experiments show that the proposed method has superior performance on two real-world MIML tasks.