We present a novel technique called a q-Gaussian distributed stochastic neighbor embedding (q-SNE) to improve t-distributed stochastic neighbor embedding (t-SNE). The q-SNE uses q-Gaussian distribution instead of t-distribution of t-SNE.
Since the q-Gaussian distribution includes Gaussian distribution, t-distribution, and others, so the q-SNE leads to more powerful and flexible visualization on 2 or 3-dimensional mapping than the t-SNE.

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

PS T1.13: Neural Networks for Pattern Recognition

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Motoshi Abe

1

SHORT BIO

Presentations

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