IEEE Trans Neural Netw Learn Syst. 2018 Mar;29(3):738-743. doi: 10.1109/TNNLS.2016.2636130. Epub 2016 Dec 29.
Recently, discriminant locality preserving projection based on L1-norm (DLPP-L1) was developed for robust subspace learning and image classification. It obtains projection vectors by greedy strategy, i.e., all projection vectors are optimized individually through maximizing the objective function. Thus, the obtained solution does not necessarily best optimize the corresponding trace ratio optimization algorithm, which is the essential objective function for general dimensionality reduction. It results in insufficient recognition accuracy. To tackle this problem, we propose a nongreedy algorithm to solve the trace ratio formula of DLPP-L1, and analyze its convergence. Experimental results on three databases illustrate the effectiveness of our proposed algorithm.
最近，提出了基于 L1 范数的判别局部保持投影（DLPP-L1），用于稳健子空间学习和图像分类。它通过贪婪策略获得投影向量，即通过最大化目标函数来单独优化所有投影向量。因此，所得到的解不一定能够最佳地优化对应于一般降维的基本目标函数的迹比优化算法。这导致识别精度不足。为了解决这个问题，我们提出了一种非贪婪算法来求解 DLPP-L1 的迹比公式，并分析了其收敛性。在三个数据库上的实验结果表明了我们提出的算法的有效性。
