NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:344
Title:No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms

Dimensionality reduction such as tSNE is widely used to visualize and interpret (and often over interpret) high-dimensional data. Thus such visualization has become a staple in the field and it is has been a while since I have seen substantial progress in improving such visualization techniques and this paper is such a case. Reviewer 1 summarizes the contribution and its importance better than I could word it myself: This work has two main contributions, which are sufficiently significant given the interest in visualization and dimensionality reduction via SNE, tSNE, and further extensions: 1. Identification of pressure points that are "stuck" in suboptimal location in the embedding due to local minima caused by dimensionality constraints. 2. Improved optimization process for SNE, tSNE, and similar methods, by allowing pressure points to temporarily bypass the dimensionality constraints in order to alleviate local minima and provide a more robust embedding. The manuscript is well written, well motivated, and convincingly establishes the reasoning behind the proposed approach as well as its effectiveness. All three reviewers agree on accepting the paper. All reviewers agreed that the paper provided new insights, a novel approach, a valuable practical contribution which is extensively validated on multiple datasets and is well written.