The reverberation time (RT) is a very important measure that quantifies the acoustic properties of a room and provides information about the quality and intelligibility of speech recorded in that room. Moreover, information about the RT can be used to improve the performance of automatic speech recognition systems and speech dereverberation algorithms. In a recent study, it has been shown that existing methods for blind estimation of the RT are highly sensitive to additive noise. In this paper, a novel method is proposed to blindly estimate the RT based on the decay rate distribution. Firstly, a data-driven representation of the underlying decay rates of several training rooms is obtained via the eigenvalue decomposition of a specially-tailored kernel. Secondly, the representation is extended to a room under test and used to estimate its decay rate (and hence its RT). The presented results show that the proposed method outperforms a competing method and is significantly more robust to noise.