The car dataset I use contains 8131 images of 64 dimensions, which means in shape [8131, 64]. These data have 98 classes which had been labeled form 0 to 97, there are about 60 to 80 images of each class.
The algorithm is as follows:
1. Use faiss library and setting nlist=100, nprobe=10, to get the 50 nearest neighbors of all nodes. (faiss library use Cluster Pruning Algorithm, to split the dataset into 100 nlist(cluster), each cluster has a leader, choose 10(nprobe) nearest clusters of the query point and find the K nearest neighbors.)
2. Get all node pairs that in the same class without duplicate. For 2 nodes in a node pair, use the longest edge of MST to build a connected graph for Manifold Ranking, separately, as the Adjacency Matrix. Leave the two Adjacency Matrix keeping Euclidean Distance without doing normalization.
3. Create a Pipe Matrix, which at first is a 0 matrix has the same shape as the Adjacency Matrix, and if the there is a node near the 1st query point has the same class as the other node near the 2nd query point, then give a beta value to the edge of these two nodes in the Pipe Matrix.
4. Splicing Matrices, for example, the Adjacency Matrix of the 1st query point at top left, the Pipe Matrix at top right and bottom left, the Adjacency Matrix of the 2nd query point at bottom right.
5. Normalizing the new matrix and doing the Manifold Ranking to get the label of the highest scored node as prediction. Particularly, give the two query points an initial signal weight 1, other nodes 0.
The following plot shows the accuracy of different beta value for images in class 0. As we can see, as the beta value increase, the accuracy got the maximum value when the beta value at 0.8, which is better than only use one query point.
My next step is doing this process to all image classes to see the results, and make another plot that shows that either two close query points or far query points perform better.