Stochastic cluster embedding

Zhirong Yang*, Yuwei Chen, Denis Sedov, Samuel Kaski, Jukka Corander

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

6 Lataukset (Pure)

Abstrakti

Neighbor embedding (NE) aims to preserve pairwise similarities between data items and has been shown to yield an effective principle for data visualization. However, even the best existing NE methods such as stochastic neighbor embedding (SNE) may leave large-scale patterns hidden, for example clusters, despite strong signals being present in the data. To address this, we propose a new cluster visualization method based on the Neighbor Embedding principle. We first present a family of Neighbor Embedding methods that generalizes SNE by using non-normalized Kullback–Leibler divergence with a scale parameter. In this family, much better cluster visualizations often appear with a parameter value different from the one corresponding to SNE. We also develop an efficient software that employs asynchronous stochastic block coordinate descent to optimize the new family of objective functions. Our experimental results demonstrate that the method consistently and substantially improves the visualization of data clusters compared with the state-of-the-art NE approaches. The code of our method is publicly available at https://github.com/rozyangno/sce.

AlkuperäiskieliEnglanti
Artikkeli12
Sivut1-14
Sivumäärä14
JulkaisuSTATISTICS AND COMPUTING
Vuosikerta33
Numero1
DOI - pysyväislinkit
TilaJulkaistu - helmik. 2023
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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  • FCAI: Suomen tekoälykeskus

    Kaski, S.

    01/01/201931/12/2022

    Projekti: Academy of Finland: Other research funding

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