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\sessionHeader{Wed1A: Distance-Based Methods and Kernels}{Stefan R\"{u}ping}{Chem, LT1}
\timeTitlePeople{10:30--10:55}{Classifying Stem Cell Differentiation Images by Information Distance}{Xianglilan Zhang, Hongnan Wang, Tony J.\ Collins, Zhigang Luo and Ming Li} 

\abst{The ability of stem cells holds great potential for drug discovery
and cell replacement therapy. To realize this potential, effective
high content screening for drug candidates is required. Analysis of
images from high content screening typically requires DNA staining
to identify cell nuclei to do cell segmentation before feature
extraction and classification. However, DNA staining has negative
effects on cell growth, and segmentation algorithms err when
compound treatments cause nuclear or cell swelling/shrinkage. In
this paper, we introduced a novel Information Distance
Classification (IDC) method, requiring no segmentation or feature
extraction; hence no DNA staining is needed. In classifying 480
candidate compounds that may be used to stimulate stem cell
differentiation, the proposed IDC method was demonstrated to achieve
a 3\% higher F$_1$ score than conventional analysis. As far as we
know, this is the first work to apply information distance in high
content screening.}


\timeTitlePeople{10:55--11:20}{Distance Metric Learning Revisited}{Qiong Cao, Yiming Ying and Peng Li} 
%I removed latex Citation \cite{Xing}

\abst{The success of many machine learning algorithms (e.g.\ the nearest neighborhood classification and k-means clustering) depends on the representation of the data as elements in a metric space.  Learning an appropriate distance metric from data is usually superior to the default Euclidean distance. In this paper, we  revisit the original model proposed by Xing et al.\ and propose a general formulation of learning a Mahalanobis distance from data.  We prove that this novel formulation  is equivalent to a convex optimization problem over the spectrahedron.  Then, a gradient-based optimization algorithm is proposed to obtain the optimal solution which only needs the computation of the largest eigenvalue of a matrix per iteration.  Finally,  experiments on various UCI datasets and a benchmark face verification dataset called Labeled Faces in the Wild (LFW) demonstrate that the proposed method compares competitively to those state-of-the-art methods.}


\timeTitlePeople{11:20--11:45}{Geodesic Analysis on the Gaussian RKHS Hypersphere}{Nicolas Courty, Thomas Burger and Pierre-Fran\c{c}ois Marteau} 

\abst{Using kernels to embed
non linear data into high dimensional spaces where linear analysis is possible has become utterly classical. 
In the case of the Gaussian kernel however, data are distributed on a hypersphere in the corresponding Reproducing Kernel Hilbert Space (RKHS).  Inspired by previous works in non-linear statistics, this article investigates the use of dedicated tools to take into account this particular geometry. Within this geometrical interpretation of the kernel theory, Riemannian distances are preferred over Euclidean distances. It is shown that this amounts to consider a new kernel and its corresponding RKHS. Experiments on real publicly available datasets show the possible benefits of the method on clustering tasks, notably through the definition of a new variant of kernel $k$-means on the hypersphere. Classification problems are also considered in a classwise setting. In both cases, the results show improvements over standard techniques.}


\timeTitlePeople{11:45--12:10}{The Bitvector Machine: A Fast and Robust Machine Learning Algorithm for Non-linear\hfill\break Problems}{Stefan Edelkamp and Martin Stommel} 

\abst{In this paper we present and evaluate a simple but
effective machine learning algorithm that we call
\emph{Bitvector Machine}: Feature vectors
are partitioned along component-wise quantiles
and converted into bitvectors that are learned.
It is shown that the method is efficient
in both training and classification. The effectiveness of the
method is analysed theoretically for best and worst-case scenarios.
Experiments on high-dimensional synthetic and real world data
show a huge speed boost compared to Support Vector Machines
with RBF kernel.
By tabulating kernel functions, computing medians in
linear-time, and exploiting modern processor technology for
advanced bitvector operations, we achieve a speed-up of
32 for classification and 48 for kernel evaluation
compared to the popular LIBSVM.
Although the method does not generally outperform a SVM with RBF kernel
it achieves a high classification accuracy and has qualitative advantages
over the linear classifier.}


\sessionend

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\sessionHeader{Wed1B: Time Series and Temporal Data Mining}{Indr\.{e} \v{Z}liobait\.{e}}{Chem, LT2}
\timeTitlePeople{10:30--10:55}{Community Trend Outlier Detection Using Soft Temporal Pattern Mining}{Manish Gupta, Jing Gao, Yizhou Sun, and Jiawei Han} 

\abst{Numerous applications, such as bank transactions, road traffic, and
news feeds, generate temporal datasets, in which data evolves
continuously. To understand the temporal behavior and
characteristics of the dataset and its elements, we need effective
tools that can capture evolution of the objects. In this paper, we propose
a novel and important problem in evolution behavior discovery. Given
a series of snapshots of a temporal dataset, each of which consists
of evolving communities, our goal is to find objects which evolve in a
dramatically different way compared with the other community members. 
We define such objects as \emph{community trend
outliers}. It is a challenging problem as evolutionary patterns are
hidden deeply in noisy evolving datasets and thus it is difficult to
distinguish anomalous objects from normal ones. We propose an effective
two-step procedure to detect community trend outliers. We first
model the normal evolutionary behavior of communities across time
using soft patterns discovered from the dataset. In the second step, we
propose effective measures to evaluate chances of an object
deviating from the normal evolutionary patterns. Experimental
results on both synthetic and real datasets show that the proposed
approach is highly effective in discovering interesting community
trend outliers.}


\timeTitlePeople{10:55--11:20}{Data Structures for Detecting Rare Variations in Time Series}{Caio Valentim, Eduardo S. Laber and David Sotelo} 

\abst{In this paper we study, from both a theoretical and an experimental perspective, algorithms and data structures to 
process queries that help in the detection of rare 
variations over time intervals that occur in time series.
Our research is strongly motivated by applications in financial domain.}


\timeTitlePeople{11:20--11:45}{Invariant Time-Series Classification}{Josif Grabocka, Alexandros Nanopoulos and Lars Schmidt-Thieme} 

\abst{Time-series classification is a field of machine learning that has attracted considerable focus during the recent decades. The large number of time-series application areas ranges from medical diagnosis up to financial econometrics. Support Vector Machines (SVMs) are reported to perform non-optimally in the domain of time series, because they suffer detecting similarities in the lack of abundant training instances. In this study we present a novel time-series transformation method which significantly improves the performance of SVMs. Our novel transformation method is used to enlarge the training set through creating new transformed instances from the support vector instances. The new transformed instances encapsulate the necessary intra-class variations required to redefine the maximum margin decision boundary. The proposed transformation method utilizes the variance distributions from the intra-class warping maps to build transformation fields, which are applied to series instances using the Moving Least Squares algorithm. Extensive experimentations on 35 time series datasets demonstrate the superiority of the proposed method compared to both the Dynamic Time Warping version of the Nearest Neighbor and the SVMs classifiers, outperforming them in the majority of the experiments.}


\timeTitlePeople{11:45--12:10}{Learning Bi-clustered Vector Autoregressive Models}{Tzu-Kuo Huang and Jeff Schneider} 

\abst{Vector Auto-regressive (VAR) models are useful for analyzing 
temporal dependencies among multivariate time series, known as \textit{Granger causality}.
There exist methods for learning sparse VAR models, leading directly 
to causal networks among the variables of interest.
Another useful type of analysis comes from clustering methods,
which summarize multiple time series by putting them into groups.
We develop a methodology that integrates both types of analyses, 
motivated by the intuition that Granger causal relations in real-world time series 
may exhibit some clustering structure, in which case the estimation of both should 
be carried out together. Our methodology combines sparse learning 
and a nonparametric \textit{bi-clustered} prior over the VAR model, 
conducting full Bayesian inference via blocked Gibbs sampling. 
Experiments on simulated and real data demonstrate improvements in 
both model estimation and clustering quality over standard alternatives, and in particular 
biologically more meaningful clusters in a T-cell activation 
gene expression time series dataset than those by other methods.}


\sessionend
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\sessionHeader{Wed1C: Spatial and Geographical Data Mining}{Willem Waegeman}{Chem, LT3}
\timeTitlePeople{10:30--10:55}{Inferring Geographic Coincidence in Ephemeral Social Networks}{Honglei Zhuang, Alvin Chin, Sen Wu, Wei Wang, Xia Wang and Jie Tang} 

% Squeeze all of this session onto one page by reducing space between elements
\vspace{-3mm}
\abst{We study users' behavioral patterns in ephemeral social networks, which are temporarily built based on events such as conferences.
From the data distribution and social theory perspectives, we found several interesting patterns. For example, the duration of two random persons staying at the same place and at the same time obeys a two-stage power-law distribution. 
We develop a framework to infer the likelihood of two users to meet together, and we apply the framework to two mobile social networks: UbiComp and Reality. The former is formed by researchers attending UbiComp 2011 and the latter is a network of students published by MIT. On both networks, we validate the proposed predictive framework, which significantly improve the accuracy for predicting geographic coincidence by comparing with two baseline methods.}

\timeTitlePeople{10:55--11:20}{Socioscope: Spatio-temporal Signal Recovery from Social Media}{Jun-Ming Xu, Aniruddha Bhargava, Robert Nowak and Xiaojin Zhu} 

\vspace{-1.5mm}
\abst{Many real-world phenomena can be represented by a spatio-temporal signal: where, when, and how much.
Social media is a tantalizing data source for those who wish to monitor such signals.
Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media.
However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity -- issues inadequately addressed by prior work.
We formulate signal recovery as a Poisson point process estimation problem.
We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-temporal regularization into the model to address the noisy count issues. 
We present an efficient optimization algorithm and discuss its theoretical properties.
We show that our model is more accurate than commonly-used baselines.
Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.}


\timeTitlePeople{11:20--11:45}{Location Affiliation Networks: Bonding Social and Spatial Information}{Konstantinos Pelechrinis and Prashant Krishnamurthy} 

\vspace{-1.5mm}
\abst{Location-based social networks (LBSNs) have recently attracted a lot of attention due to the number of novel services they can offer. Prior work on analysis of LBSNs has mainly focused on the social part of these systems. Even though it is important to know how different the structure of the social graph of an LBSN is as compared to the friendship-based social networks (SNs), it raises the interesting question of what kinds of linkages exist between locations and friendships.  
The main problem we are investigating is to identify such connections between the social and the spatial planes of an LBSN. In particular, in this paper we focus on answering the following general question ``What are the bonds between the social and spatial information in an LBSN and what are the metrics that can reveal them?" 
In order to tackle this problem, we employ the idea of {\em affiliation networks}. Analyzing a dataset from a specific LBSN (Gowalla), we make two main interesting observations; (i) the social network exhibits {\em signs of homophily} with regards to the ``places/venues" visited by the users, and (ii) the ``nature" of the visited venues that are common to users is powerful and informative in revealing the social/spatial linkages. We further show that the ``entropy" (or diversity) of a venue can be used to better connect spatial information with the existing social relations. The entropy records the diversity of a venue and requires only location history of users (it does not need temporal history).  
Finally, we provide a simple application of our findings for predicting existing friendship relations based on users' historic spatial information. We show that even with simple unsupervised learning models we can achieve significant improvement in prediction when we consider features that capture the ``nature" of the venue as compared to the case where only apparent properties of the location history are used (e.g., number of common visits).}


\timeTitlePeople{11:45--12:10}{Pedestrian Quantity Estimation with Trajectory Patterns}{Thomas Liebig, Zhao Xu, Michael May and Stefan Wrobel} 

\vspace{-1.5mm}
\abst{In street-based mobility mining, traffic volume estimation receives increasing attention as it provides important applications such as emergency support systems, quality-of-service evaluation and billboard placement. In many real world scenarios, empirical measurements are usually sparse due to some constraints. On the other hand, pedestrians generally show some movement preferences, especially in closed environments, e.g., train stations. We propose a Gaussian process regression based method for traffic volume estimation, which incorporates topological information and prior knowledge on preferred trajectories with a trajectory pattern kernel. Our approach also enables effectively finding most informative sensor placements. We evaluate our method with synthetic German train station pedestrian data and real-world episodic movement data from the zoo of Duisburg. The empirical analysis demonstrates that incorporating trajectory patterns can largely improve the traffic prediction accuracy, especially when traffic networks are sparsely monitored.}


\sessionend

