q-SNE: Visualizing Data using q-Gaussian Distributed Stochastic Neighbor Embedding

12/02/2020
by   Motoshi Abe, et al.
0

The dimensionality reduction has been widely introduced to use the high-dimensional data for regression, classification, feature analysis, and visualization. As the one technique of dimensionality reduction, a stochastic neighbor embedding (SNE) was introduced. The SNE leads powerful results to visualize high-dimensional data by considering the similarity between the local Gaussian distributions of high and low-dimensional space. To improve the SNE, a t-distributed stochastic neighbor embedding (t-SNE) was also introduced. To visualize high-dimensional data, the t-SNE leads to more powerful and flexible visualization on 2 or 3-dimensional mapping than the SNE by using a t-distribution as the distribution of low-dimensional data. Recently, Uniform manifold approximation and projection (UMAP) is proposed as a dimensionality reduction technique. We present a novel technique called a q-Gaussian distributed stochastic neighbor embedding (q-SNE). The q-SNE leads to more powerful and flexible visualization on 2 or 3-dimensional mapping than the t-SNE and the SNE by using a q-Gaussian distribution as the distribution of low-dimensional data. The q-Gaussian distribution includes the Gaussian distribution and the t-distribution as the special cases with q=1.0 and q=2.0. Therefore, the q-SNE can also express the t-SNE and the SNE by changing the parameter q, and this makes it possible to find the best visualization by choosing the parameter q. We show the performance of q-SNE as visualization on 2-dimensional mapping and classification by k-Nearest Neighbors (k-NN) classifier in embedded space compared with SNE, t-SNE, and UMAP by using the datasets MNIST, COIL-20, OlivettiFaces, FashionMNIST, and Glove.

READ FULL TEXT

page 1

page 5

research
02/09/2022

Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network

t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visuali...
research
04/12/2021

Deep Recursive Embedding for High-Dimensional Data

t-distributed stochastic neighbor embedding (t-SNE) is a well-establishe...
research
09/17/2020

Learning a Deep Part-based Representation by Preserving Data Distribution

Unsupervised dimensionality reduction is one of the commonly used techni...
research
02/27/2020

Supervised Dimensionality Reduction and Visualization using Centroid-encoder

Visualizing high-dimensional data is an essential task in Data Science a...
research
07/17/2020

Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data

Dimension reduction and visualization of high-dimensional data have beco...
research
08/02/2022

Cluster Weighted Model Based on TSNE algorithm for High-Dimensional Data

Similar to many Machine Learning models, both accuracy and speed of the ...
research
02/18/2022

Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images

High-dimensional imaging is becoming increasingly relevant in many field...

Please sign up or login with your details

Forgot password? Click here to reset