Image retrieval and ranking based on the multi-attribute queries is beneficial to various real world applications. Traditional methods on this problem often utilize intermediate representations generated by attribute classifiers to describe the images, and then the images in the database are sorted according to their similarities to the query. However, such a scheme has two main challenges: 1) how to exploit the correlation between query attributes and non-query attributes, and 2) how to handle noisy representations since the pre-defined attribute classifiers are probably unreliable. To overcome these challenges, we discover the correlation among attributes via expanding the query representation, and imposing the group sparsity on representations to reduce the disturbance of noisy data. Specifically, given a multi-attribute query matrix with each row corresponding to a query attribute and each column the pre-defined attribute, we firstly expand the query based on the correlation of the attributes learned from the training data. Then, the expanded query matrix is reconstructed by the images in the dataset with the ℓ
regularization. Furthermore, we introduce the ranking SVM into the objective function to guarantee the ranking consistency. Finally, we adopt a graph regularization to preserve the local visual similarity among images. Extensive experiments on LFW, CUB-200-2011, and Shoes datasets are conducted to demonstrate the effectiveness of our proposed method.