Spiked Tensor

Spiked tensor models analyze the recovery of hidden signals embedded within high-dimensional tensor data, often focusing on scenarios with noise and multiple signals. Current research emphasizes understanding the performance of various optimization algorithms, such as stochastic gradient descent and gradient flow, in recovering these signals, particularly within the context of tensor principal component analysis and multi-view clustering. These studies are crucial for advancing our understanding of high-dimensional data analysis and have implications for applications like signal processing, machine learning, and data clustering where tensor representations are prevalent. The development of efficient algorithms for signal recovery from noisy spiked tensors is a key focus, with a particular interest in comparing the performance of tensor-based methods against matrix-based alternatives.