Task-Driven Dictionary Learning
Task-Driven Dictionary Learning
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DOI：
10.1109/tpami.2011.156
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发表时间：
2012-04-01
影响因子：
23.6
通讯作者：
Ponce, Jean
中科院分区：
文献类型：
--
作者：
Mairal, Julien;Bach, Francis;Ponce, Jean
使用学习字典中的一些元素的线性组合对数据进行建模一直是机器学习、神经科学和信号处理领域近期研究的焦点。对于诸如自然图像之类允许这种稀疏表示的信号，现在已经确定这些模型非常适合恢复任务。在这种情况下，学习字典相当于解决大规模矩阵分解问题，这可以使用经典优化工具有效地完成。同样的方法也被用于从数据中学习特征以用于其他目的，例如。例如，图像分类，但事实证明，以监督方式调整字典来完成这些任务更加困难。在本文中，我们提出了适用于各种任务的监督字典学习的通用公式，并提出了解决相应优化问题的有效算法。手写数字分类、数字艺术识别、非线性逆图像问题和压缩感知的实验表明，我们的方法在大规模环境中是有效的，并且非常适合监督和半监督分类，以及允许稀疏表示的数据的回归任务。
Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e. g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations.