Nonparametric Learning Via Successive Subspace Modeling (SSM)
Yueru Chen · 2019
A novel nonparametric machine learning methodology, called successive subspace modeling (SSM), is proposed in this work. Without loss of generality, we use image classification as an illustrative example. The SSM procedure consists of two stages: 1) feature learning and 2) decision learning. For feature learning, we partition input images into overlapping patches of different sizes recursively. While the input images define a vector space, patches of smaller sizes form a sequence of growing subspaces. From the smallest to the largest subspaces, we build a model for each subspace in a successive manner through the Saab transform. At the end, we obtain a lower-dimensional feature vector space that contains significant spatial-spectral information of input images. For decision learning, we summarize the distribution of feature vectors with two techniques; namely, feature space partitioning and local manifold learning. Then, for every test sample, an ensemble decision is made based on decisions at each of its near clusters. The superior performance of SSM is demonstrated on the MNIST dataset.