Respiratory gated radiotherapy for lung cancer allows for more precise delivery of prescribed radiation dose to the tumor, while minimizing normal tissue complications. Techniques for fluoroscopic gating without implanted fiducial markers have been developed in a classification framework. Due to the high-dimensionality nature of the images, dimensionality reduction techniques such as principal component analysis (PCA) were used to preprocess the data. In this work, we have applied generalized linear discriminant analysis (GLDA) to the respiratory gating problem. The fundamental difference from conventional dimensionality reduction techniques is that GLDA explicitly takes into account the label information available in the training set and therefore is efficient for discrimination among classes. On average, GLDA was demonstrated to perform similarly with PCA trained with SVM at high nominal duty cycles and outperform PCA in terms of classification accuracy (CA) and target coverage (TC) at lower nominal duty cycle (20%). A major advantage of GLDA is its robustness, while CA and TC using PCA can be reduced by up to 10% depending on the data dimensionality. With only 1-dimensional feature vectors, GLDA is much more computationally efficient than PCA. Therefore, GLDA is an effective and efficient method for respiratory gating with markerless fluoroscopic images.