Predictive Subspace Learning for Multi-view Data: a Large Margin Approach
Predictive Subspace Learning for Multi-view Data: a Large Margin Approach
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发表时间：
2010-12
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通讯作者：
Ning Chen;Jun Zhu;E. Xing
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作者：
Ning Chen;Jun Zhu;E. Xing
从多视图数据中学习在许多应用中都很重要，例如图像分类和注释。在本文中，我们提出了一个大裕度学习框架来发现多个视图共享的预测潜在子空间表示。我们的方法基于无向潜在空间马尔可夫网络，该网络满足弱条件独立假设，即给定一组潜在变量，多视图观察和响应变量是独立的。我们为潜在子空间模型提供有效的推理和参数估计方法。最后，我们展示了在真实视频和网络图像数据上进行大规模学习的优势，以发现预测潜在表示并提高图像分类、注释和检索的性能。
Learning from multi-view data is important in many applications, such as image classification and annotation. In this paper, we present a large-margin learning framework to discover a predictive latent subspace representation shared by multiple views. Our approach is based on an undirected latent space Markov network that fulfills a weak conditional independence assumption that multi-view observations and response variables are independent given a set of latent variables. We provide efficient inference and parameter estimation methods for the latent sub-space model. Finally, we demonstrate the advantages of large-margin learning on real video and web image data for discovering predictive latent representations and improving the performance on image classification, annotation and retrieval.