Search Results for author: Rishi Sonthalia

Found 7 papers, 3 papers with code

How can classical multidimensional scaling go wrong?

no code implementations NeurIPS 2021 Rishi Sonthalia, Gregory Van Buskirk, Benjamin Raichel, Anna C. Gilbert

While $D_l$ is not metric, when given as input to cMDS instead of $D$, it empirically results in solutions whose distance to $D$ does not increase when we increase the dimension and the classification accuracy degrades less than the cMDS solution.

Tree! I am no Tree! I am a Low Dimensional Hyperbolic Embedding

2 code implementations NeurIPS 2020 Rishi Sonthalia, Anna C. Gilbert

Given data, finding a faithful low-dimensional hyperbolic embedding of the data is a key method by which we can extract hierarchical information or learn representative geometric features of the data.

Project and Forget: Solving Large-Scale Metric Constrained Problems

1 code implementation8 May 2020 Rishi Sonthalia, Anna C. Gilbert

Given a set of dissimilarity measurements amongst data points, determining what metric representation is most "consistent" with the input measurements or the metric that best captures the relevant geometric features of the data is a key step in many machine learning algorithms.

Metric Learning

Project and Forget: Solving Large Scale Metric Constrained Problems

no code implementations25 Sep 2019 Anna C. Gilbert, Rishi Sonthalia

Given a set of distances amongst points, determining what metric representation is most “consistent” with the input distances or the metric that captures the relevant geometric features of the data is a key step in many machine learning algorithms.

Metric Learning

Unsupervised Metric Learning in Presence of Missing Data

3 code implementations19 Jul 2018 Anna C. Gilbert, Rishi Sonthalia

Here, we present a new algorithm MR-MISSING that extends these previous algorithms and can be used to compute low dimensional representation on data sets with missing entries.

Dimensionality Reduction Matrix Completion +1

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