417 research outputs found
Analysis of Crowdsourced Sampling Strategies for HodgeRank with Sparse Random Graphs
Crowdsourcing platforms are now extensively used for conducting subjective
pairwise comparison studies. In this setting, a pairwise comparison dataset is
typically gathered via random sampling, either \emph{with} or \emph{without}
replacement. In this paper, we use tools from random graph theory to analyze
these two random sampling methods for the HodgeRank estimator. Using the
Fiedler value of the graph as a measurement for estimator stability
(informativeness), we provide a new estimate of the Fiedler value for these two
random graph models. In the asymptotic limit as the number of vertices tends to
infinity, we prove the validity of the estimate. Based on our findings, for a
small number of items to be compared, we recommend a two-stage sampling
strategy where a greedy sampling method is used initially and random sampling
\emph{without} replacement is used in the second stage. When a large number of
items is to be compared, we recommend random sampling with replacement as this
is computationally inexpensive and trivially parallelizable. Experiments on
synthetic and real-world datasets support our analysis
HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation
Recently, crowdsourcing has emerged as an effective paradigm for
human-powered large scale problem solving in various domains. However, task
requester usually has a limited amount of budget, thus it is desirable to have
a policy to wisely allocate the budget to achieve better quality. In this
paper, we study the principle of information maximization for active sampling
strategies in the framework of HodgeRank, an approach based on Hodge
Decomposition of pairwise ranking data with multiple workers. The principle
exhibits two scenarios of active sampling: Fisher information maximization that
leads to unsupervised sampling based on a sequential maximization of graph
algebraic connectivity without considering labels; and Bayesian information
maximization that selects samples with the largest information gain from prior
to posterior, which gives a supervised sampling involving the labels collected.
Experiments show that the proposed methods boost the sampling efficiency as
compared to traditional sampling schemes and are thus valuable to practical
crowdsourcing experiments.Comment: Accepted by AAAI201
The relationships between PM2.5 and meteorological factors in China: Seasonal and regional variations
The interactions between PM2.5 and meteorological factors play a crucial role
in air pollution analysis. However, previous studies that have researched the
relationships between PM2.5 concentration and meteorological conditions have
been mainly confined to a certain city or district, and the correlation over
the whole of China remains unclear. Whether or not spatial and seasonal
variations exit deserves further research. In this study, the relationships
between PM2.5 concentration and meteorological factors were investigated in 74
major cities in China for a continuous period of 22 months from February 2013
to November 2014, at season, year, city, and regional scales, and the spatial
and seasonal variations were analyzed. The meteorological factors were relative
humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS).
We found that spatial and seasonal variations of their relationships with PM2.5
do exist. Spatially, RH is positively correlated with PM2.5 concentration in
North China and Urumqi, but the relationship turns to negative in other areas
of China. WS is negatively correlated with PM2.5 everywhere expect for Hainan
Island. PS has a strong positive relationship with PM2.5 concentration in
Northeast China and Mid-south China, and in other areas the correlation is
weak. Seasonally, the positive correlation between PM2.5 concentration and RH
is stronger in winter and spring. TEM has a negative relationship with PM2.5 in
autumn and the opposite in winter. PS is more positively correlated with PM2.5
in autumn than in other seasons. Our study investigated the relationships
between PM2.5 and meteorological factors in terms of spatial and seasonal
variations, and the conclusions about the relationships between PM2.5 and
meteorological factors are more comprehensive and precise than before.Comment: 3 tables, 13 figure
Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size
Learning representation from relative similarity comparisons, often called
ordinal embedding, gains rising attention in recent years. Most of the existing
methods are batch methods designed mainly based on the convex optimization,
say, the projected gradient descent method. However, they are generally
time-consuming due to that the singular value decomposition (SVD) is commonly
adopted during the update, especially when the data size is very large. To
overcome this challenge, we propose a stochastic algorithm called SVRG-SBB,
which has the following features: (a) SVD-free via dropping convexity, with
good scalability by the use of stochastic algorithm, i.e., stochastic variance
reduced gradient (SVRG), and (b) adaptive step size choice via introducing a
new stabilized Barzilai-Borwein (SBB) method as the original version for convex
problems might fail for the considered stochastic \textit{non-convex}
optimization problem. Moreover, we show that the proposed algorithm converges
to a stationary point at a rate in our setting,
where is the number of total iterations. Numerous simulations and
real-world data experiments are conducted to show the effectiveness of the
proposed algorithm via comparing with the state-of-the-art methods,
particularly, much lower computational cost with good prediction performance.Comment: 11 pages, 3 figures, 2 tables, accepted by AAAI201
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