Joint learning dictionary and discriminative features for high dimensional data
Joint learning dictionary and discriminative features for high dimensional data
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DOI：
10.1109/icpr.2016.7899661
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
Xian Wei;Yuanxiang Li;Hao Shen;M. Kleinsteuber;Y. Murphey
中科院分区：
文献类型：
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作者：
Xian Wei;Yuanxiang Li;Hao Shen;M. Kleinsteuber;Y. Murphey
最近，冗余字典上的稀疏表示(SR)已经成为一种流行的数据表示方式。它已被证明是一种有效和有用的工具来促进信号之间的区分。这项工作开发了一种联合学习方法来寻找高维数据的低维可区分特征。为了避免直接对大规模输入数据进行稀疏编码的计算代价较高，我们首先在任务驱动的稀疏字典上的正交投影空间中学习SR。然后，我们利用SR上的区分投影。整个学习过程被看作是一个迹商最大化的优化问题，涉及到原始数据空间上的一个正交投影、一个字典和一个稀疏码上的判别投影。在Stiefel流形、斜流形和Grassmann流形的乘积流形上很好地定义了相关的代价函数。最后，我们在光滑乘积流形上使用随机梯度下降算法来最大化成本函数。我们在视觉识别上的数值实验表明，与现有技术相比，该算法是有效的。
Recently, sparse representation (SR) over a redundant dictionary has become a popular way of representing the data. It has been verified as an efficient and useful tool to promote the discrimination between signals. This work develops a joint learning approach to find the low dimensional discriminative features for high dimensional data. To avoid the high computational cost of direct sparse coding on large scale input data, we first learn SR in an orthogonal projected space over a task-driven sparsifying dictionary. We then exploit the discriminative projection on SR. The whole learning process is treated as an optimization problem of trace quotient maximization, which involves an orthogonal projection on original data space, a dictionary and a discriminative projection on sparse codes. The related cost function is well defined on a product manifold of the Stiefel manifold, the Oblique manifold and the Grassmann manifold. Finally, we employ a stochastic gradient descent algorithm on the smooth product manifold to maximize the cost function. Our numerical experiments on visual recognition demonstrate the effectiveness of the proposed algorithm, in comparison with the state of the arts.