pwc-archive/papers-with-abstracts huggingface dataset:
Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures
Authors: Pierre-François Marteau, Sylvie Gibet, Clement Reverdy
Date: 2014-08-18 00:00:00
Tasks: Action Recognition, Dimensionality Reduction, Gesture Recognition, Temporal Action Localization

In the field of gestural action recognition, many studies have focused on dimensionality reduction along the spatial axis, to reduce both the variability of gestural sequences expressed in the reduced space, and the computational complexity of their processing. It is noticeable that very few of these methods have explicitly addressed the dimensionality reduction along the time axis. This is however a major issue with regard to the use of elastic distances characterized by a quadratic complexity. To partially fill this apparent gap, we present in this paper an approach based on temporal down-sampling associated to elastic kernel machine learning. We experimentally show, on two data sets that are widely referenced in the domain of human gesture recognition, and very different in terms of quality of motion capture, that it is possible to significantly reduce the number of skeleton frames while maintaining a good recognition rate. The method proves to give satisfactory results at a level currently reached by state-of-the-art methods on these data sets. The computational complexity reduction makes this approach eligible for real-time applications.
Distance Shrinkage and Euclidean Embedding via Regularized Kernel Estimation
Authors: Luwan Zhang, Grace Wahba, Ming Yuan
Date: 2014-09-17 00:00:00
Tasks: Diversity

Although recovering an Euclidean distance matrix from noisy observations is a
common problem in practice, how well this could be done remains largely
unknown. To fill in this void, we study a simple distance matrix estimate based
upon the so-called regularized kernel estimate. We show that such an estimate
can be characterized as simply applying a constant amount of shrinkage to all
observed pairwise distances. This fact allows us to establish risk bounds for
the estimate implying that the true distances can be estimated consistently in
an average sense as the number of objects increases. In addition, such a
characterization suggests an efficient algorithm to compute the distance matrix
estimator, as an alternative to the usual second order cone programming known
not to scale well for large problems. Numerical experiments and an application
in visualizing the diversity of Vpu protein sequences from a recent HIV-1 study
further demonstrate the practical merits of the proposed method.
An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
Authors: Filippo Maria Bianchi, Enrico Maiorino, Lorenzo Livi, Antonello Rizzi, Alireza Sadeghian
Date: 2014-09-17 00:00:00
Tasks: Clustering

We propose a multi-agent algorithm able to automatically discover relevant
regularities in a given dataset, determining at the same time the set of
configurations of the adopted parametric dissimilarity measure yielding compact
and separated clusters. Each agent operates independently by performing a
Markovian random walk on a suitable weighted graph representation of the input
dataset. Such a weighted graph representation is induced by the specific
parameter configuration of the dissimilarity measure adopted by the agent,
which searches and takes decisions autonomously for one cluster at a time.
Results show that the algorithm is able to discover parameter configurations
that yield a consistent and interpretable collection of clusters. Moreover, we
demonstrate that our algorithm shows comparable performances with other similar
state-of-the-art algorithms when facing specific clustering problems.
Adaptive Tag Selection for Image Annotation
Authors: Xixi He, Xirong Li, Gang Yang, Jieping Xu, Qin Jin
Date: 2014-09-17 00:00:00
Tasks: TAG

Not all tags are relevant to an image, and the number of relevant tags is
image-dependent. Although many methods have been proposed for image
auto-annotation, the question of how to determine the number of tags to be
selected per image remains open. The main challenge is that for a large tag
vocabulary, there is often a lack of ground truth data for acquiring optimal
cutoff thresholds per tag. In contrast to previous works that pre-specify the
number of tags to be selected, we propose in this paper adaptive tag selection.
The key insight is to divide the vocabulary into two disjoint subsets, namely a
seen set consisting of tags having ground truth available for optimizing their
thresholds and a novel set consisting of tags without any ground truth. Such a
division allows us to estimate how many tags shall be selected from the novel
set according to the tags that have been selected from the seen set. The
effectiveness of the proposed method is justified by our participation in the
ImageCLEF 2014 image annotation task. On a set of 2,065 test images with ground
truth available for 207 tags, the benchmark evaluation shows that compared to
the popular top-$k$ strategy which obtains an F-score of 0.122, adaptive tag
selection achieves a higher F-score of 0.223. Moreover, by treating the
underlying image annotation system as a black box, the new method can be used
as an easy plug-in to boost the performance of existing systems.
Reinforcement Learning Based Algorithm for the Maximization of EV Charging Station Revenue
Authors: Stoyan Dimitrov, Redouane Lguensat
Date: 2014-07-04 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents an online reinforcement learning based application which
increases the revenue of one particular electric vehicles (EV) station,
connected to a renewable source of energy. Moreover, the proposed application
adapts to changes in the trends of the station's average number of customers
and their types. Most of the parameters in the model are simulated
stochastically and the algorithm used is a Q-learning algorithm. A computer
simulation was implemented which demonstrates and confirms the utility of the
model.
Tensity Research Based on the Information of Eye Movement
Authors: Yi Wang
Date: 2014-09-17 00:00:00

User's mental state is concerned gradually, during the interaction course of
human robot. As the measurement and identification method of psychological
state, tension, has certain practical significance role. At presents there is
no suitable method of measuring the tension. Firstly, sum up some availability
of eye movement index. And then parameters extraction on eye movement
characteristics of normal illumination is studied, including the location of
the face, eyes location, access to the pupil diameter, the eye pupil center
characteristic parameters. And with the judgment of the tension in eye images,
extract exact information of gaze direction. Finally, through the experiment to
prove the proposed method is effective.
Scalable Verification of Markov Decision Processes
Authors: Axel Legay, Sean Sedwards, Louis-Marie Traonouez
Date: 2013-10-14 00:00:00

Markov decision processes (MDP) are useful to model concurrent process
optimisation problems, but verifying them with numerical methods is often
intractable. Existing approximative approaches do not scale well and are
limited to memoryless schedulers. Here we present the basis of scalable
verification for MDPSs, using an O(1) memory representation of
history-dependent schedulers. We thus facilitate scalable learning techniques
and the use of massively parallel verification.
Ensembles of Random Sphere Cover Classifiers
Authors: Anthony Bagnall, Reda Younsi
Date: 2014-09-17 00:00:00
Tasks: Attribute

We propose and evaluate alternative ensemble schemes for a new instance based
learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses
instances into spheres, then bases classification on distance to spheres rather
than distance to instances. The randomised nature of RSC makes it ideal for use
in ensembles. We propose two ensemble methods tailored to the RSC classifier;
$\alpha \beta$RSE, an ensemble based on instance resampling and $\alpha$RSSE, a
subspace ensemble. We compare $\alpha \beta$RSE and $\alpha$RSSE to tree based
ensembles on a set of UCI datasets and demonstrates that RSC ensembles perform
significantly better than some of these ensembles, and not significantly worse
than the others. We demonstrate via a case study on six gene expression data
sets that $\alpha$RSSE can outperform other subspace ensemble methods on high
dimensional data when used in conjunction with an attribute filter. Finally, we
perform a set of Bias/Variance decomposition experiments to analyse the source
of improvement in comparison to a base classifier.
Statistical inference with probabilistic graphical models
Authors: Angélique Drémeau, Christophe Schülke, Yingying Xu, Devavrat Shah
Date: 2014-09-17 00:00:00

These are notes from the lecture of Devavrat Shah given at the autumn school
"Statistical Physics, Optimization, Inference, and Message-Passing Algorithms",
that took place in Les Houches, France from Monday September 30th, 2013, till
Friday October 11th, 2013. The school was organized by Florent Krzakala from
UPMC & ENS Paris, Federico Ricci-Tersenghi from La Sapienza Roma, Lenka
Zdeborova from CEA Saclay & CNRS, and Riccardo Zecchina from Politecnico
Torino. This lecture of Devavrat Shah (MIT) covers the basics of inference and
learning. It explains how inference problems are represented within structures
known as graphical models. The theoretical basis of the belief propagation
algorithm is then explained and derived. This lecture sets the stage for
generalizations and applications of message passing algorithms.
Efficiently Detecting Overlapping Communities through Seeding and Semi-Supervised Learning
Authors: Changxing Shang, Shengzhong Feng, Zhongying Zhao, Jianping Fan
Date: 2014-01-23 00:00:00
Tasks: Clustering, Community Detection

Seeding then expanding is a commonly used scheme to discover overlapping
communities in a network. Most seeding methods are either too complex to scale
to large networks or too simple to select high-quality seeds, and the
non-principled functions used by most expanding methods lead to poor
performance when applied to diverse networks. This paper proposes a new method
that transforms a network into a corpus where each edge is treated as a
document, and all nodes of the network are treated as terms of the corpus. An
effective seeding method is also proposed that selects seeds as a training set,
then a principled expanding method based on semi-supervised learning is applied
to classify edges. We compare our new algorithm with four other community
detection algorithms on a wide range of synthetic and empirical networks.
Experimental results show that the new algorithm can significantly improve
clustering performance in most cases. Furthermore, the time complexity of the
new algorithm is linear to the number of edges, and this low complexity makes
the new algorithm scalable to large networks.
Face Identification with Second-Order Pooling
Authors: Fumin Shen, Chunhua Shen, Heng Tao Shen
Date: 2014-06-26 00:00:00
Tasks: Face Identification, Face Recognition, image-classification, Image Classification, Image Segmentation, Object Recognition, Semantic Segmentation

Automatic face recognition has received significant performance improvement by developing specialised facial image representations. On the other hand, generic object recognition has rarely been applied to the face recognition. Spatial pyramid pooling of features encoded by an over-complete dictionary has been the key component of many state-of-the-art image classification systems. Inspired by its success, in this work we develop a new face image representation method inspired by the second-order pooling in Carreira et al. [1], which was originally proposed for image segmentation. The proposed method differs from the previous methods in that, we encode the densely extracted local patches by a small-size dictionary; and the facial image signatures are obtained by pooling the second-order statistics of the encoded features. We show the importance of pooling on encoded features, which is bypassed by the original second-order pooling method to avoid the high computational cost. Equipped with a simple linear classifier, the proposed method outperforms the state-of-the-art face identification performance by large margins. For example, on the LFW databases, the proposed method performs better than the previous best by around 13% accuracy.
Face Image Classification by Pooling Raw Features
Authors: Fumin Shen, Chunhua Shen, Heng Tao Shen
Date: 2014-06-26 00:00:00
Tasks: Classification, Face Recognition, General Classification, image-classification, Image Classification

We propose a very simple, efficient yet surprisingly effective feature extraction method for face recognition (about 20 lines of Matlab code), which is mainly inspired by spatial pyramid pooling in generic image classification. We show that features formed by simply pooling local patches over a multi-level pyramid, coupled with a linear classifier, can significantly outperform most recent face recognition methods. The simplicity of our feature extraction procedure is demonstrated by the fact that no learning is involved (except PCA whitening). We show that, multi-level spatial pooling and dense extraction of multi-scale patches play critical roles in face image classification. The extracted facial features can capture strong structural information of individual faces with no label information being used. We also find that, pre-processing on local image patches such as contrast normalization can have an important impact on the classification accuracy. In particular, on the challenging face recognition datasets of FERET and LFW-a, our method improves previous best results by more than 10% and 20%, respectively.
Continuum limit of total variation on point clouds
Authors: Nicolás García Trillos, Dejan Slepčev
Date: 2014-03-25 00:00:00
Tasks: Clustering

We consider point clouds obtained as random samples of a measure on a
Euclidean domain. A graph representing the point cloud is obtained by assigning
weights to edges based on the distance between the points they connect. Our
goal is to develop mathematical tools needed to study the consistency, as the
number of available data points increases, of graph-based machine learning
algorithms for tasks such as clustering. In particular, we study when is the
cut capacity, and more generally total variation, on these graphs a good
approximation of the perimeter (total variation) in the continuum setting. We
address this question in the setting of $\Gamma$-convergence. We obtain almost
optimal conditions on the scaling, as number of points increases, of the size
of the neighborhood over which the points are connected by an edge for the
$\Gamma$-convergence to hold. Taking the limit is enabled by a transportation
based metric which allows to suitably compare functionals defined on different
point clouds.
Going Deeper with Convolutions
Authors: Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
Date: 2014-09-17 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification, Image Classification, Object Detection, Object Recognition

We propose a deep convolutional neural network architecture codenamed
"Inception", which was responsible for setting the new state of the art for
classification and detection in the ImageNet Large-Scale Visual Recognition
Challenge 2014 (ILSVRC 2014). The main hallmark of this architecture is the
improved utilization of the computing resources inside the network. This was
achieved by a carefully crafted design that allows for increasing the depth and
width of the network while keeping the computational budget constant. To
optimize quality, the architectural decisions were based on the Hebbian
principle and the intuition of multi-scale processing. One particular
incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22
layers deep network, the quality of which is assessed in the context of
classification and detection.
Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets
Authors: Michael Bloodgood, K. Vijay-Shanker
Date: 2014-09-17 00:00:00
Tasks: Active Learning

Actively sampled data can have very different characteristics than passively
sampled data. Therefore, it's promising to investigate using different
inference procedures during AL than are used during passive learning (PL). This
general idea is explored in detail for the focused case of AL with
cost-weighted SVMs for imbalanced data, a situation that arises for many HLT
tasks. The key idea behind the proposed InitPA method for addressing imbalance
is to base cost models during AL on an estimate of overall corpus imbalance
computed via a small unbiased sample rather than the imbalance in the labeled
training data, which is the leading method used during PL.
Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions
Authors: Tanmay Sinha, Patrick Jermann, Nan Li, Pierre Dillenbourg
Date: 2014-07-26 00:00:00
Proceeding: WS 2014 10

In this work, we explore video lecture interaction in Massive Open Online
Courses (MOOCs), which is central to student learning experience on these
educational platforms. As a research contribution, we operationalize video
lecture clickstreams of students into cognitively plausible higher level
behaviors, and construct a quantitative information processing index, which can
aid instructors to better understand MOOC hurdles and reason about
unsatisfactory learning outcomes. Our results illustrate how such a metric
inspired by cognitive psychology can help answer critical questions regarding
students' engagement, their future click interactions and participation
trajectories that lead to in-video & course dropouts. Implications for research
and practice are discussed
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Authors: Patrice Simard, David Chickering, Aparna Lakshmiratan, Denis Charles, Leon Bottou, Carlos Garcia Jurado Suarez, David Grangier, Saleema Amershi, Johan Verwey, Jina Suh
Date: 2014-09-16 00:00:00
Tasks: Active Learning

Quick interaction between a human teacher and a learning machine presents
numerous benefits and challenges when working with web-scale data. The human
teacher guides the machine towards accomplishing the task of interest. The
learning machine leverages big data to find examples that maximize the training
value of its interaction with the teacher. When the teacher is restricted to
labeling examples selected by the machine, this problem is an instance of
active learning. When the teacher can provide additional information to the
machine (e.g., suggestions on what examples or predictive features should be
used) as the learning task progresses, then the problem becomes one of
interactive learning.
  To accommodate the two-way communication channel needed for efficient
interactive learning, the teacher and the machine need an environment that
supports an interaction language. The machine can access, process, and
summarize more examples than the teacher can see in a lifetime. Based on the
machine's output, the teacher can revise the definition of the task or make it
more precise. Both the teacher and the machine continuously learn and benefit
from the interaction.
  We have built a platform to (1) produce valuable and deployable models and
(2) support research on both the machine learning and user interface challenges
of the interactive learning problem. The platform relies on a dedicated,
low-latency, distributed, in-memory architecture that allows us to construct
web-scale learning machines with quick interaction speed. The purpose of this
paper is to describe this architecture and demonstrate how it supports our
research efforts. Preliminary results are presented as illustrations of the
architecture but are not the primary focus of the paper.
Anomaly Detection Based on Indicators Aggregation
Authors: Tsirizo Rabenoro, Jérôme Lacaille, Marie Cottrell, Fabrice Rossi
Date: 2014-09-16 00:00:00
Tasks: Anomaly Detection, feature selection

Automatic anomaly detection is a major issue in various areas. Beyond mere
detection, the identification of the source of the problem that produced the
anomaly is also essential. This is particularly the case in aircraft engine
health monitoring where detecting early signs of failure (anomalies) and
helping the engine owner to implement efficiently the adapted maintenance
operations (fixing the source of the anomaly) are of crucial importance to
reduce the costs attached to unscheduled maintenance. This paper introduces a
general methodology that aims at classifying monitoring signals into normal
ones and several classes of abnormal ones. The main idea is to leverage expert
knowledge by generating a very large number of binary indicators. Each
indicator corresponds to a fully parametrized anomaly detector built from
parametric anomaly scores designed by experts. A feature selection method is
used to keep only the most discriminant indicators which are used at inputs of
a Naive Bayes classifier. This give an interpretable classifier based on
interpretable anomaly detectors whose parameters have been optimized indirectly
by the selection process. The proposed methodology is evaluated on simulated
data designed to reproduce some of the anomaly types observed in real world
engines.
Anomaly Detection Based on Aggregation of Indicators
Authors: Tsirizo Rabenoro, Jérôme Lacaille, Marie Cottrell, Fabrice Rossi
Date: 2014-07-03 00:00:00
Tasks: Anomaly Detection, feature selection

Automatic anomaly detection is a major issue in various areas. Beyond mere
detection, the identification of the origin of the problem that produced the
anomaly is also essential. This paper introduces a general methodology that can
assist human operators who aim at classifying monitoring signals. The main idea
is to leverage expert knowledge by generating a very large number of
indicators. A feature selection method is used to keep only the most
discriminant indicators which are used as inputs of a Naive Bayes classifier.
The parameters of the classifier have been optimized indirectly by the
selection process. Simulated data designed to reproduce some of the anomaly
types observed in real world engines.
Supervised Classification Using Sparse Fisher's LDA
Authors: Irina Gaynanova, James G. Booth, Martin T. Wells
Date: 2013-01-21 00:00:00
Tasks: Classification, General Classification, Robust classification

It is well known that in a supervised classification setting when the number
of features is smaller than the number of observations, Fisher's linear
discriminant rule is asymptotically Bayes. However, there are numerous modern
applications where classification is needed in the high-dimensional setting.
Naive implementation of Fisher's rule in this case fails to provide good
results because the sample covariance matrix is singular. Moreover, by
constructing a classifier that relies on all features the interpretation of the
results is challenging. Our goal is to provide robust classification that
relies only on a small subset of important features and accounts for the
underlying correlation structure. We apply a lasso-type penalty to the
discriminant vector to ensure sparsity of the solution and use a shrinkage type
estimator for the covariance matrix. The resulting optimization problem is
solved using an iterative coordinate ascent algorithm. Furthermore, we analyze
the effect of nonconvexity on the sparsity level of the solution and highlight
the difference between the penalized and the constrained versions of the
problem. The simulation results show that the proposed method performs
favorably in comparison to alternatives. The method is used to classify
leukemia patients based on DNA methylation features.