t-SNE:
Student TDistributed-Stochastic Neighbor Embedding
▷Nonlinear Dimension Reduction for Visualization (2-D or 3-D)
▷Advance Version of SNE (G. Hinton, NIPS 2003)
▷Gradient-based Machine Learning Algorithm
Dimension Reduction
RealWorld Data = Very High Dimension
= 3145728 Dimension per Sample (ProGAN)
SNE t-SNE
▷Crowding Problem Student t-Distribution
Student t-Distribution in Low-Dimension
This High-Dimension Data
SNE t-SNE
▷Crowding Problem Student t-Distribution
Student t-Distribution in Low-Dimension
This High-Dimension Data
Loses its Probability
Closer
SNE t-SNE
▷Crowding Problem Student t-Distribution
Student t-Distribution in Low-Dimension
This High-Dimension Data
SNE t-SNE
▷Crowding Problem Student t-Distribution
Student t-Distribution in Low-Dimension
This High-Dimension Data
Gains its Probability
Morefaraway
High-D Low-D �
���
��(�
��−�
��)(�
�−�
�) Gradient
Large Large 1 1 0 Large 0
Small Small 0 0 0 Small 0
Small Large 0 1 -1 Large Large
Attraction
Large Small 1 0 1 Small Small
Repulsion
Small Replusion
Adding Slight Repulsion (Uniform Dist. in �
��)
Often Not the Case
Low-D Initialized by Gaussian
High-D Low-D �
���
��(�
��−�
��) (�
�−�
�) 1+�
�−�
�
2
−1
Gradient
Large Large 1 1 0 Large Small 0
Small Small 0 0 0 Small Large 0
Small Large 0 1 -1 Large Small Attraction
Large Small 1 0 1 Small Large Repulsion
Strong Replusion