The identity vector(i-vector) approach has been the state-of-the-art for text-independent speaker recognition, both identification and verification in recent years. An i-vector is a low-dimensional vector in the socalled total variability space represented with a thin and tall rectangular matrix. This paper introduces a novel algorithm to improve the computational and memory requirements for the application. In our method, the series of symmetric matrices can be represented by diagonal expression,sharing the same dictionary, which to some extent is analogous to eigen decomposition, and we name this algorithm Eigen decomposition like factorization(EDLF). Similar algorithms are listed for comparison, in the same condition,our method shows no disadvantages in identification accuracy. The identity vector (i-vector) approach has been the state-of-the-art for text-independent speaker recognition, both identification and verification in recent years. An i-vector is a low-dimensional vector in the socalled total variability space This paper introduces a novel algorithm to improve the computational and memory requirements for the application. In our method, the series of symmetric matrices can be represented by diagonal expression, sharing the same dictionary, which to some extent is analogous to eigen decomposition, and we name this algorithm Eigen decomposition like factorization (EDLF). Similar algorithms are listed for comparison, in the same condition, our method shows no disadvantages in identification accuracy.