College of Computer Science, Chongqing University of Posts and Telecommunications, Chongqing, China.
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China.
Big Data. 2023 Feb;11(1):18-34. doi: 10.1089/big.2021.0420. Epub 2022 May 10.
Feature extraction algorithms lack good interpretability during the projection learning. To solve this problem, an unsupervised feature extraction algorithm, that is, block diagonal projection (BDP), based on self-expression is proposed. Specifically, if the original data are projected into a low-dimensional subspace by a feature extraction algorithm, although the data may be more compact, the new features obtained may not be as explanatory as the original sample features. Therefore, by imposing L2,1 norm constraint on the projection matrix, the projection matrix can be of row sparsity. On one hand, discriminative features can be selected to make the projection matrix to be more interpretable. On the other hand, irrelevant or redundant features can be suppressed. The proposed model integrates feature extraction and selection into one framework. In addition, since self-expression can well excavate the correlation between samples or sample features, the unsupervised feature extraction task can be better guided using this property between them. At the same time, the block diagonal representation regular term is introduced to directly pursue the block diagonal representation. Thus, the accuracy of pattern recognition tasks such as clustering and classification can be improved. Finally, the effectiveness of BDP in linear dimensionality reduction and classification is proved on various reference datasets. The experimental results show that this algorithm is superior to previous feature extraction counterparts.
特征提取算法在投影学习过程中缺乏良好的可解释性。为了解决这个问题，提出了一种基于自表达的无监督特征提取算法，即块对角投影（BDP）。具体来说，如果原始数据通过特征提取算法投影到低维子空间中，虽然数据可能更紧凑，但得到的新特征可能不如原始样本特征具有解释性。因此，通过对投影矩阵施加 L2,1 范数约束，可以使投影矩阵具有行稀疏性。一方面，可以选择判别特征以使投影矩阵更具可解释性。另一方面，可以抑制不相关或冗余的特征。所提出的模型将特征提取和选择集成到一个框架中。此外，由于自表达可以很好地挖掘样本或样本特征之间的相关性，因此可以利用这种相关性更好地指导无监督的特征提取任务。同时，引入块对角表示正则项来直接追求块对角表示。因此，可以提高聚类和分类等模式识别任务的准确性。最后，在各种参考数据集上验证了 BDP 在线性降维和分类方面的有效性。实验结果表明，该算法优于以前的特征提取算法。
