Dimensionality Reduction by Local Discriminative Gaussians
This is an incremental improvement for classification tasks, offering a more scalable solution compared to existing linear dimensionality reduction methods.
The paper introduces local discriminative Gaussian (LDG), a supervised dimensionality reduction method for classification that approximates leave-one-out training error to find mappings for discriminating similar from dissimilar data locally, achieving good performance in transfer learning with differing data distributions.
We present local discriminative Gaussian (LDG) dimensionality reduction, a supervised dimensionality reduction technique for classification. The LDG objective function is an approximation to the leave-one-out training error of a local quadratic discriminant analysis classifier, and thus acts locally to each training point in order to find a mapping where similar data can be discriminated from dissimilar data. While other state-of-the-art linear dimensionality reduction methods require gradient descent or iterative solution approaches, LDG is solved with a single eigen-decomposition. Thus, it scales better for datasets with a large number of feature dimensions or training examples. We also adapt LDG to the transfer learning setting, and show that it achieves good performance when the test data distribution differs from that of the training data.