This paper proposes a deep learning priors-based data reconstruction method of 1D isometric feature mapping (ISOMAP) representations. ISOMAP is a classical algorithm of nonlinear dimensionality reduction (NLDR) or manifold leaning (ML), which is devoted to questing for the low dimensional structure of high dimensional data. The reconstruction of ISOMAP representations, or the inverse problem of ISOMAP, reestablishes the high dimensional data from its low dimensional ISOMAP representations, and owns a bright future in data representation, generation, compression and visualization. Due to the fact that the dimension of ISOMAP representations is far less than that of the original high dimensional data, the reconstruction of ISOMAP representations is ill-posed or undetermined. Hence, the residual learning of deep convolutional neural network (CNN) is employed to boost reconstruction performance, via achieving the priors between the low-quality result of general ISOMAP reconstruction method and its residual relative to the original data. In the situation of 1D representations, it is evaluated by the experimental results that the proposed method outbalances the state-of-the-art methods, such as nearest neighbor (NN), discrete cosine transformation (DCT) and sparse representation (SR), in reconstruction performance of video data. In summary, the proposed method is suitable for low-bitrate and high-performance applications of data reconstruction.

本文提出了一种基于深度学习先验的一维等距特征映射（ISOMAP）表示的数据重建方法。ISOMAP是非线性降维（NLDR）或流形学习（ML）的经典算法，专用于寻求高维数据的低维结构。ISOMAP表示的重建或ISOMAP的反问题，从其低维ISOMAP表示重新建立了高维数据，并且在数据表示，生成，压缩和可视化方面拥有光明的未来。由于ISOMAP表示的尺寸远小于原始高维数据的尺寸，因此ISOMAP表示的重建是不适当的或不确定的。因此，深度卷积神经网络（CNN）的残差学习可通过实现一般ISOMAP重建方法的低质量结果与其相对于原始数据的残差之间的先验优势，来提高重建性能。在一维表示的情况下，通过实验结果评估了该方法与最新方法（例如最近邻（NN），离散余弦变换（DCT）和稀疏表示（SR））不平衡，视频数据的重建性能。综上所述，该方法适用于数据重构的低比特率和高性能应用。在一维表示的情况下，通过实验结果评估了该方法与最新方法（例如最近邻（NN），离散余弦变换（DCT）和稀疏表示（SR））不平衡，视频数据的重建性能。综上所述，该方法适用于数据重构的低比特率和高性能应用。在一维表示的情况下，通过实验结果评估了该方法与最新方法（例如最近邻（NN），离散余弦变换（DCT）和稀疏表示（SR））不平衡，视频数据的重建性能。综上所述，该方法适用于数据重构的低比特率和高性能应用。