Influenced by factors such as observation conditions and acquisition scenarios, underwater optical image data usually presents the characteristics of high-dimensional small samples and is easily accompanied with noise interference. Resulting in insufficient robust performance of many dimensionality reduction methods in their recognition process. To solve this problem, we propose a novel 2DPCA method for underwater optical image recognition, called arbitrary triangle structure 2DPCA (ATS-2DPCA). On the basis of considering the relationship between reconstruction error and variance of projection data, ATS-2DPCA can successfully match the flexible robust distance metric mechanism, which effectively improves the accuracy of underwater optical image recognition under noise interference environment and achieves reasonable protection of the geometric structure of the data. In this paper, we theoretically prove the availability and convergence of the proposed method and use three underwater optical image databases for experimental verification. The optimal recognition accuracy is 89.07%, 88.52%, and 86.00%, respectively. The extensive experimental results show that ATS-2DPCA has more outstanding performance than other 2DPCA-based methods.