Tensor Train Neighborhood Preserving Embedding
This work addresses the challenge of efficient tensor data embedding for supervised learning, though it appears incremental as it builds on existing tensor embedding methods.
The paper tackles the problem of embedding multi-dimensional tensor data into a low-dimensional tensor subspace by proposing Tensor Train Neighborhood Preserving Embedding (TTNPE), which achieves a superior trade-off among classification, computation, and dimensionality reduction compared to state-of-the-art methods on MNIST and Weizmann datasets.
In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multi-dimensional tensor data into low dimensional tensor subspace. Novel approaches to solve the optimization problem in TTNPE are proposed. For this embedding, we evaluate novel trade-off gain among classification, computation, and dimensionality reduction (storage) for supervised learning. It is shown that compared to the state-of-the-arts tensor embedding methods, TTNPE achieves superior trade-off in classification, computation, and dimensionality reduction in MNIST handwritten digits and Weizmann face datasets.