Large margin metric learning for multi-label prediction

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Canonical correlation analysis (CCA) and maximum margin output coding (MMOC) methods have shown promising results for multi-label prediction, where each instance is associated with multiple labels. However, these methods require an expensive decoding procedure to recover the multiple labels of each testing instance. The testing complexity becomes unacceptable when there are many labels. To avoid decoding completely, we present a novel large margin metric learning paradigm for multi-label prediction. In particular, the proposed method learns a distance metric to discover label dependency such that instances with very different multiple labels will be moved far away. To handle many labels, we present an accelerated proximal gradient procedure to speed up the learning process. Comprehensive experiments demonstrate that our proposed method is significantly faster than CCA and MMOC in terms of both training and testing complexities. Moreover, our method achieves superior prediction performance compared with state-of-the-art methods

Advanced topics in multi-label learning

University of Technology Sydney. Faculty of Engineering and Information Technology.Multi-label learning, in which each instance can belong to multiple labels simultaneously, has significantly attracted the attention of researchers as a result of its wide range of applications, which range from document classification and automatic image annotation to video annotation. Many multi-label learning models have been developed to capture label dependency. Amongst them, the classifier chain (CC) model is one of the most popular methods due to its simplicity and promising experimental results. However, CC suffers from three important problems: Does the label order affect the performance of CC? Is there any globally optimal classifier chain which can achieve the optimal prediction performance for CC? If yes, how can the globally optimal classifier chain be found? It is non-trivial to answer these problems. Another important branch of methods for capturing label dependency is encoding-decoding paradigm. Based on structural SVMs, maximum margin output coding (MMOC) has become one of the most representative encoding-decoding methods and shown promising results for multi-label classification. Unfortunately, MMOC suffers from two major limitations: 1) Inconsistent performance: D. McAllester has already proved that structural SVMs fail to converge on the optimal decoder even with infinite training data. 2) Prohibitive computational cost: the training of MMOC involves a complex quadratic programming (QP) problem over the combinatorial space, and its computational cost on the data sets with many labels is prohibitive. Therefore, it is non-trivial to break the bottlenecks of MMOC, and develop efficient and consistent algorithms for solving multi-label learning tasks. The prediction of most multi-label learning methods either scales linearly with the number of labels or involves an expensive decoding process, which usually requires solving a combinatorial optimization. Such approaches become unacceptable when tackling thousands of labels, and are impractical for real-world applications, such as document annotation. It is imperative to design an efficient, yet accurate multi-label learning algorithm with the minimum number of predictions. This thesis systematically studies how to efficiently solve aforementioned issues with provable guarantee

Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201

Conditional Graphical Lasso for Multi-label Image Classification

© 2016 IEEE. Multi-label image classification aims to predict multiple labels for a single image which contains diverse content. By utilizing label correlations, various techniques have been developed to improve classification performance. However, current existing methods either neglect image features when exploiting label correlations or lack the ability to learn image-dependent conditional label structures. In this paper, we develop conditional graphical Lasso (CGL) to handle these challenges. CGL provides a unified Bayesian framework for structure and parameter learning conditioned on image features. We formulate the multi-label prediction as CGL inference problem, which is solved by a mean field variational approach. Meanwhile, CGL learning is efficient due to a tailored proximal gradient procedure by applying the maximum a posterior (MAP) methodology. CGL performs competitively for multi-label image classification on benchmark datasets MULAN scene, PASCAL VOC 2007 and PASCAL VOC 2012, compared with the state-of-the-art multi-label classification algorithms

G\mathcal{G}-softmax: Improving Intra-class Compactness and Inter-class Separability of Features

Intra-class compactness and inter-class separability are crucial indicators to measure the effectiveness of a model to produce discriminative features, where intra-class compactness indicates how close the features with the same label are to each other and inter-class separability indicates how far away the features with different labels are. In this work, we investigate intra-class compactness and inter-class separability of features learned by convolutional networks and propose a Gaussian-based softmax ($\mathcal{G}$-softmax) function that can effectively improve intra-class compactness and inter-class separability. The proposed function is simple to implement and can easily replace the softmax function. We evaluate the proposed $\mathcal{G}$-softmax function on classification datasets (i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet) and on multi-label classification datasets (i.e., MS COCO and NUS-WIDE). The experimental results show that the proposed $\mathcal{G}$-softmax function improves the state-of-the-art models across all evaluated datasets. In addition, analysis of the intra-class compactness and inter-class separability demonstrates the advantages of the proposed function over the softmax function, which is consistent with the performance improvement. More importantly, we observe that high intra-class compactness and inter-class separability are linearly correlated to average precision on MS COCO and NUS-WIDE. This implies that improvement of intra-class compactness and inter-class separability would lead to improvement of average precision.Comment: 15 pages, published in TNNL