Latest least squares regression (LSR) methods mainly try to learn slack regression targets to replace strict zero-one labels. However, the difference of intra-class targets can also be highlighted when enlarging the distance between different classes, and roughly persuing relaxed targets may lead to the problem of overfitting. To solve above problems, we propose a low-rank discriminative least squares regression model (LRDLSR) for multi-class image classification. Specifically, LRDLSR class-wisely imposes low-rank constraint on the intra-class regression targets to encourage its compactness and similarity. Moreover, LRDLSR introduces an additional regularization term on the learned targets to avoid the problem of overfitting. These two improvements are helpful to learn a more discriminative projection for regression and thus achieving better classification performance. Experimental results over a range of image databases demonstrate the effectiveness of the proposed LRDLSR method.
翻译:然而,在扩大不同类别之间的距离时,也可以突出分类内目标的差别,而大致的宽松目标可能会导致过分适应问题。为了解决上述问题,我们建议为多级图像分类采用低层次的歧视性最低方回归模型(LRDLSR),具体地说,LRDLSR等级对分类内回归目标实行低级别限制,以鼓励其紧凑性和相似性。此外,LRDLSR在所学目标上增加了一个正规化术语,以避免过分适应问题。这两项改进有助于了解对回归的更具有歧视性的预测,从而取得更好的分类业绩。一系列图像数据库的实验结果显示了拟议的LRDLSR方法的有效性。