## 1 Introduction
By reducing the dimensions of HRIRs through non-negative matrix factorization, [PERSON] et al. [10] reduced the HRIR dimensions of different subjects on a same direction to extract the feature vectors closely related to the anthropometric features. Then, eight anthropometric features were selected with correlation analysis to conduct the mapping relationship from the former to the latter by support vector regression. Next, the individualized HRIR basis vectors could be inferred by the trained nonlinear regression model, after which the individualized HRIR can be obtained by multiplying the feature vectors and basis vectors. [PERSON] et al. [11] conducted a nonlinear mapping from eight independent anthropometric features to the twelve weight vectors of the principal components by using back-propagation neural network to obtain individualized HRTFs. Under the assumption that the linear weighting relationship between HRTF magnitudes are the same for different subjects and anthropometric features, [PERSON] et al. [12] proposed the sparse efficient vectors, which can be used to represent anthropometric features of target subject with that of available subjects in the database. The individualized HRTF magnitudes could be obtained by multiplying the sparse vectors to the HRTF magnitudes in the database. [PERSON] et al. [13] supposed that the interaural time difference and HRTF magnitude possessed the same sparse relationship with the anthropometric features; they elicited a sparse vector from the anthropometric features, which could be used to recover the phase and magnitude of HRTFs. [PERSON] et al. [14] studied the accuracy of different preprocessing methods on the sparse reconstruction of HRTFs. [PERSON] et al. [15] proposed another sparse based HRTF individualization method. Considering that the anthropometric features were not equally important for HRTFs [16], a weight set was assigned to the anthropometric features using a partially on-off strategy. And a sparse representation of the weighted anthropometric features was found. The individualized HRTFs could be synthesized through the weighted sparse representation of anthropometric features. [PERSON] and [PERSON] [17] proposed a simple and efficient method for HRTF individualization by model individual HRTF magnitudes with a linear combination of ten orthonormal basis obtained from principle component analysis. Such methods mainly rely on the algorithm improvements to meet the demand of synthetic HRTF precision. Obtaining HRTFs fast as well as precisely is strategic for the application in auralization. Although the above-mentioned studies have conducted in-depth research on HRTF individualization, more sufficient methods are still needed to obtain individualized HRTFs that contain accurate spatial information. To synthesize HRTFs for any listener, we present an integrated algorithm combing the method of correlation analysis, principle component analysis (PCA), multiple linear regression (MLR) and partial least square regression (PLSR). First, PCA was used to decompose HRTFs and extract the characteristic parameters. Then, correlation analysis was conducted to make a premier selection for reference features. Next, the retained anthropometric features were put into the MLR model to make a final selection for reference features. Finally, PLSR was used to synthesize the individualized HRTFs of a new subject. The advancement of synthesized HRTFs were then validated by two subjective listening tests using auralized sounds produced by convolving an original signal with the synthesized HRTFs. In our current research, only the horizontal plane data were used for HRTF individualization and listening tests.