Dimension Reduction with Locally Adjusted Graphs

TL;DR

LocalMAP is a novel dimension reduction algorithm that dynamically adjusts local graphs to improve cluster separation in high-dimensional data, especially in biological datasets.

Contribution

The paper introduces LocalMAP, a new DR method that adaptively updates graphs locally to better identify true clusters in large high-dimensional datasets.

Findings

LocalMAP outperforms existing DR methods in cluster separation.

It effectively identifies real clusters in biological datasets.

Dynamic graph adjustment enhances dimensionality reduction accuracy.

Abstract

Dimension reduction (DR) algorithms have proven to be extremely useful for gaining insight into large-scale high-dimensional datasets, particularly finding clusters in transcriptomic data. The initial phase of these DR methods often involves converting the original high-dimensional data into a graph. In this graph, each edge represents the similarity or dissimilarity between pairs of data points. However, this graph is frequently suboptimal due to unreliable high-dimensional distances and the limited information extracted from the high-dimensional data. This problem is exacerbated as the dataset size increases. If we reduce the size of the dataset by selecting points for a specific sections of the embeddings, the clusters observed through DR are more separable since the extracted subgraphs are more reliable. In this paper, we introduce LocalMAP, a new dimensionality reduction algorithm that dynamically and locally adjusts the graph to address this challenge. By dynamically extracting subgraphs and updating the graph on-the-fly, LocalMAP is capable of identifying and separating real clusters within the data that other DR methods may overlook or combine. We demonstrate the benefits of LocalMAP through a case study on biological datasets, highlighting its utility in helping users more accurately identify clusters for real-world problems.