Image classification is one of the most important and basic problems in image processing, and designing an effective feature extraction method and a fast classifier with a high recognition rate are two key points in image classification. Polyharmonic random weights networks (P-RWNs) are proposed based on the random weights networks (RWNs) and the advantage of polynomial that it can approximate the part with small variation effectively. Based on the proposed P-RWNs, a method for image classification is presented by integrating fast discrete curvelet transform ( FDCT) and discriminative locality alignment (DLA ). In the proposed method, FDCT is used to extract features from images, then the dimensionalities of these features are reduced by DLA before the features are input to the proposed P-RWNs classifier for recognition. Experimental results show that the proposed image classification method achieves higher recognition rate and recognition speed.%图像分类是图像处理研究中重要且基本的问题之一，而设计有效的特征提取方法和快速高精度的分类器则是图像分类研究的关键。文中以随机权网络算法为基础，结合多项式函数能有效逼近目标函数相对平缓部分的优点，提出调和随机权网络算法，并以此算法作为分类器，结合快速离散曲波变换和局部判别定位法，给出一种图像分类方法。该方法首先利用快速离散曲波变换提取图像特征，然后依据局部判别定位法对所提取的图像特征降维，最后运用所提出的调和随机权网络分类器识别降维的特征，从而有效实现图像分类。实验表明文中方法具有更高的识别率和更快的识别速度。
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