论文标题
深度确定性的独立组件分析，用于高光谱脉
Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing
论文作者
论文摘要
我们通过直接最大程度地减少所有提取的组件之间的依赖性来开发一种新的基于神经网络的独立组件分析（ICA）方法。使用基于矩阵的r {é} NYI的$α$订单熵功能，我们的网络可以通过随机梯度下降（SGD）直接优化，而无需任何变化近似或对抗性训练。作为一个可靠的应用，我们评估了ICA在高光谱混合问题（HU）的问题中，并驳斥了“ \ emph {ICA在Unmixpral Data}中没有作用的陈述”，该声明最初由\ cite {Nascimento2005does}提出。我们的ddica的代码和其他评论可在https://github.com/hongmingli1995/ddica上找到。
We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components. Using the matrix-based R{é}nyi's $α$-order entropy functional, our network can be directly optimized by stochastic gradient descent (SGD), without any variational approximation or adversarial training. As a solid application, we evaluate our ICA in the problem of hyperspectral unmixing (HU) and refute a statement that "\emph{ICA does not play a role in unmixing hyperspectral data}", which was initially suggested by \cite{nascimento2005does}. Code and additional remarks of our DDICA is available at https://github.com/hongmingli1995/DDICA.
