Linear discriminant analysis (LDA) is a classic tool for supervised dimensionality reduction. Because the projected samples can be classified effectively, LDA has been successfully applied in many applications. Among the variants of LDA, trace ratio LDA (TR-LDA) is a classic form due to its explicit meaning. Unfortunately, when the sample size is much smaller than the data dimension, the algorithm for solving TR-LDA does not converge. The so-called small sample size (SSS) problem severely limits the application of TR-LDA. To solve this problem, we propose a revised formation of TR-LDA, which can be applied to datasets with different sizes in a unified form. Then, we present an optimization algorithm to solve the proposed method, explain why it can avoid the SSS problem, and analyze the convergence and computational complexity of the optimization algorithm. Next, based on the introduced theorems, we quantitatively elaborate on when the SSS problem will occur in TR-LDA. Finally, the experimental results on real-world datasets demonstrate the effectiveness of the proposed method.