183,130 research outputs found
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
We propose a probabilistic model for refining coarse-grained spatial data by
utilizing auxiliary spatial data sets. Existing methods require that the
spatial granularities of the auxiliary data sets are the same as the desired
granularity of target data. The proposed model can effectively make use of
auxiliary data sets with various granularities by hierarchically incorporating
Gaussian processes. With the proposed model, a distribution for each auxiliary
data set on the continuous space is modeled using a Gaussian process, where the
representation of uncertainty considers the levels of granularity. The
fine-grained target data are modeled by another Gaussian process that considers
both the spatial correlation and the auxiliary data sets with their
uncertainty. We integrate the Gaussian process with a spatial aggregation
process that transforms the fine-grained target data into the coarse-grained
target data, by which we can infer the fine-grained target Gaussian process
from the coarse-grained data. Our model is designed such that the inference of
model parameters based on the exact marginal likelihood is possible, in which
the variables of fine-grained target and auxiliary data are analytically
integrated out. Our experiments on real-world spatial data sets demonstrate the
effectiveness of the proposed model.Comment: Appears in Proceedings of the Thirty-Third AAAI Conference on
Artificial Intelligence (AAAI 2019
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
Scalable Group Level Probabilistic Sparse Factor Analysis
Many data-driven approaches exist to extract neural representations of
functional magnetic resonance imaging (fMRI) data, but most of them lack a
proper probabilistic formulation. We propose a group level scalable
probabilistic sparse factor analysis (psFA) allowing spatially sparse maps,
component pruning using automatic relevance determination (ARD) and subject
specific heteroscedastic spatial noise modeling. For task-based and resting
state fMRI, we show that the sparsity constraint gives rise to components
similar to those obtained by group independent component analysis. The noise
modeling shows that noise is reduced in areas typically associated with
activation by the experimental design. The psFA model identifies sparse
components and the probabilistic setting provides a natural way to handle
parameter uncertainties. The variational Bayesian framework easily extends to
more complex noise models than the presently considered.Comment: 10 pages plus 5 pages appendix, Submitted to ICASSP 1
Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models
The gap between our ability to collect interesting data and our ability to
analyze these data is growing at an unprecedented rate. Recent algorithmic
attempts to fill this gap have employed unsupervised tools to discover
structure in data. Some of the most successful approaches have used
probabilistic models to uncover latent thematic structure in discrete data.
Despite the success of these models on textual data, they have not generalized
as well to image data, in part because of the spatial and temporal structure
that may exist in an image stream.
We introduce a novel unsupervised machine learning framework that
incorporates the ability of convolutional autoencoders to discover features
from images that directly encode spatial information, within a Bayesian
nonparametric topic model that discovers meaningful latent patterns within
discrete data. By using this hybrid framework, we overcome the fundamental
dependency of traditional topic models on rigidly hand-coded data
representations, while simultaneously encoding spatial dependency in our topics
without adding model complexity. We apply this model to the motivating
application of high-level scene understanding and mission summarization for
exploratory marine robots. Our experiments on a seafloor dataset collected by a
marine robot show that the proposed hybrid framework outperforms current
state-of-the-art approaches on the task of unsupervised seafloor terrain
characterization.Comment: 8 page
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