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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Semisupervised Feature Analysis by Mining Correlations Among Multiple Tasks
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Semisupervised Feature Analysis by Mining Correlations Among Multiple Tasks

机译：通过挖掘多个任务之间的相关性进行半监督特征分析

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摘要

In this paper, we propose a novel semisupervised feature selection framework by mining correlations among multiple tasks and apply it to different multimedia applications. Instead of independently computing the importance of features for each task, our algorithm leverages shared knowledge from multiple related tasks, thus improving the performance of feature selection. Note that the proposed algorithm is built upon an assumption that different tasks share some common structures. The proposed algorithm selects features in a batch mode, by which the correlations between various features are taken into consideration. Besides, considering the fact that labeling a large amount of training data in real world is both time-consuming and tedious, we adopt manifold learning, which exploits both labeled and unlabeled training data for a feature space analysis. Since the objective function is nonsmooth and difficult to solve, we propose an iteractive algorithm with fast convergence. Extensive experiments on different applications demonstrate that our algorithm outperforms the other state-of-the-art feature selection algorithms.
机译：在本文中，我们通过挖掘多个任务之间的相关性，提出了一种新颖的半监督特征选择框架，并将其应用于不同的多媒体应用程序。我们的算法没有独立计算每个任务的功能重要性，而是利用了多个相关任务的共享知识，从而提高了功能选择的性能。注意，所提出的算法是建立在不同任务共享一些共同结构的假设之上的。所提出的算法以批处理方式选择特征，通过这种方式考虑了各种特征之间的相关性。此外，考虑到在现实世界中标记大量训练数据既费时又乏味的事实，我们采用多种学习，该方法利用标记的和未标记的训练数据进行特征空间分析。由于目标函数不平滑且难以求解，我们提出了一种具有快速收敛性的迭代算法。在不同应用程序上的大量实验表明，我们的算法优于其他最新的特征选择算法。

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