71,600 research outputs found
A Probabilistic Embedding Clustering Method for Urban Structure Detection
Urban structure detection is a basic task in urban geography. Clustering is a
core technology to detect the patterns of urban spatial structure, urban
functional region, and so on. In big data era, diverse urban sensing datasets
recording information like human behaviour and human social activity, suffer
from complexity in high dimension and high noise. And unfortunately, the
state-of-the-art clustering methods does not handle the problem with high
dimension and high noise issues concurrently. In this paper, a probabilistic
embedding clustering method is proposed. Firstly, we come up with a
Probabilistic Embedding Model (PEM) to find latent features from high
dimensional urban sensing data by learning via probabilistic model. By latent
features, we could catch essential features hidden in high dimensional data
known as patterns; with the probabilistic model, we can also reduce uncertainty
caused by high noise. Secondly, through tuning the parameters, our model could
discover two kinds of urban structure, the homophily and structural
equivalence, which means communities with intensive interaction or in the same
roles in urban structure. We evaluated the performance of our model by
conducting experiments on real-world data and experiments with real data in
Shanghai (China) proved that our method could discover two kinds of urban
structure, the homophily and structural equivalence, which means clustering
community with intensive interaction or under the same roles in urban space.Comment: 6 pages, 7 figures, ICSDM201
Semantic bottleneck for computer vision tasks
This paper introduces a novel method for the representation of images that is
semantic by nature, addressing the question of computation intelligibility in
computer vision tasks. More specifically, our proposition is to introduce what
we call a semantic bottleneck in the processing pipeline, which is a crossing
point in which the representation of the image is entirely expressed with
natural language , while retaining the efficiency of numerical representations.
We show that our approach is able to generate semantic representations that
give state-of-the-art results on semantic content-based image retrieval and
also perform very well on image classification tasks. Intelligibility is
evaluated through user centered experiments for failure detection
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