-
Metric Learning for Phoneme Perception
Metric functions for phoneme perception capture the similarity structure...
read it
-
Kernel functions based on triplet comparisons
Given only information in the form of similarity triplets "Object A is m...
read it
-
Local Similarity-Aware Deep Feature Embedding
Existing deep embedding methods in vision tasks are capable of learning ...
read it
-
The similarity metric
A new class of distances appropriate for measuring similarity relations ...
read it
-
Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Embeddings and the Implications to Representation Learning
Human judgments of word similarity have been a popular method of evaluat...
read it
-
An empirical study on the names of points of interest and their changes with geographic distance
While Points Of Interest (POIs), such as restaurants, hotels, and barber...
read it
-
TRAJEDI: Trajectory Dissimilarity
The vast increase in our ability to obtain and store trajectory data nec...
read it
Measuring Place Function Similarity with Trajectory Embedding
Modeling place functions from a computational perspective is a prevalent research topic. The technology of embedding enables a new approach that allows modeling the function of a place by its chronological context as part of a trajectory. The embedding similarity was previously proposed as a new metric for measuring the similarity of place functions, with some preliminary results. This study explores if this approach is meaningful for geographical units at a much smaller geographical granularity compared to previous studies. In addition, this study investigates if the geographical distance can influence the embedding similarity. The empirical evaluations based on a big vehicle trajectory data set confirm that the embedding similarity can be a metric proxy for place functions. However, the results also show that the embedding similarity is still bounded by the distance at the local scale.
READ FULL TEXT
Comments
There are no comments yet.