Min-Max-Jump distance and its applications
We explore three applications of Min-Max-Jump distance (MMJ distance). MMJ-based K-means revises K-means with MMJ distance. MMJ-based Silhouette coefficient revises Silhouette coefficient with MMJ distance. We also tested the Clustering with Neural Network and Index (CNNI) model with MMJ-based Silhouette coefficient. In the last application, we tested using Min-Max-Jump distance for predicting labels of new points, after a clustering analysis of data. Result shows Min-Max-Jump distance achieves good performances in all the three proposed applications. In addition, we devise several algorithms for calculating or estimating the distance.
Code (1)
Tasks
ClusteringSimilar Papers 제목 키워드 기반
An efficient Wasserstein-distance approach for reconstructing jump-diffusion processes using parameterized neural networks
We analyze the Wasserstein distance ($W$-distance) between two probability distributions associated with two multidimensional jump-diffusion processes. Specifically, we analyze a temporally decoupled squared $W_2$-distan…
Exploring Decision-Making Capabilities of LLM Agents: An Experimental Study on Jump-Jump Game
The Jump-Jump game, as a simple yet challenging casual game, provides an ideal testing environment for studying LLM decision-making capabilities. The game requires players to precisely control jumping force based on curr…
Spatial ReasoningRobust Quadruped Jumping via Deep Reinforcement Learning
In this paper, we consider a general task of jumping varying distances and heights for a quadrupedal robot in noisy environments, such as off of uneven terrain and with variable robot dynamics parameters. To accurately j…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Suppressing Final Layer Hidden State Jumps in Transformer Pretraining
This paper discusses the internal behavior of Transformer language models. Many recent pre-trained models have been reported to exhibit only slight changes in the angular distance between the input and output hidden stat…
Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters
High-order Graph Neural Networks (HO-GNNs) have been developed to infer consistent latent spaces in the heterophilic regime, where the label distribution is not correlated with the graph structure. However, most of the e…
Node Classification