pwc-archive/papers-with-abstracts huggingface dataset:
Sparse multi-view matrix factorisation: a multivariate approach to multiple tissue comparisons
Authors: Zi Wang, Wei Yuan, Giovanni Montana
Date: 2015-03-04 00:00:00

Gene expression levels in a population vary extensively across tissues. Such
heterogeneity is caused by genetic variability and environmental factors, and
is expected to be linked to disease development. The abundance of experimental
data now enables the identification of features of gene expression profiles
that are shared across tissues, and those that are tissue-specific. While most
current research is concerned with characterising differential expression by
comparing mean expression profiles across tissues, it is also believed that a
significant difference in a gene expression's variance across tissues may also
be associated to molecular mechanisms that are important for tissue development
and function. We propose a sparse multi-view matrix factorisation (sMVMF)
algorithm to jointly analyse gene expression measurements in multiple tissues,
where each tissue provides a different "view" of the underlying organism. The
proposed methodology can be interpreted as an extension of principal component
analysis in that it provides the means to decompose the total sample variance
in each tissue into the sum of two components: one capturing the variance that
is shared across tissues, and one isolating the tissue-specific variances.
sMVMF has been used to jointly model mRNA expression profiles in three tissues
- adipose, skin and LCL - which are available for a large and well-phenotyped
twins cohort, TwinsUK. Using sMVMF, we are able to prioritise genes based on
whether their variation patterns are specific to each tissue. Furthermore,
using DNA methylation profiles available, we provide supporting evidence that
adipose-specific gene expression patterns may be driven by epigenetic effects.
Deep Neural Networks for Anatomical Brain Segmentation
Authors: Alexandre de Brebisson, Giovanni Montana
Date: 2015-02-09 00:00:00
Tasks: Brain Segmentation, Segmentation

We present a novel approach to automatically segment magnetic resonance (MR)
images of the human brain into anatomical regions. Our methodology is based on
a deep artificial neural network that assigns each voxel in an MR image of the
brain to its corresponding anatomical region. The inputs of the network capture
information at different scales around the voxel of interest: 3D and orthogonal
2D intensity patches capture the local spatial context while large, compressed
2D orthogonal patches and distances to the regional centroids enforce global
spatial consistency. Contrary to commonly used segmentation methods, our
technique does not require any non-linear registration of the MR images. To
benchmark our model, we used the dataset provided for the MICCAI 2012 challenge
on multi-atlas labelling, which consists of 35 manually segmented MR images of
the brain. We obtained competitive results (mean dice coefficient 0.725, error
rate 0.163) showing the potential of our approach. To our knowledge, our
technique is the first to tackle the anatomical segmentation of the whole brain
using deep neural networks.
Diffusion Fingerprints
Authors: Jimmy Dubuisson, Jean-Pierre Eckmann, Andrea Agazzi
Date: 2014-08-21 00:00:00
Tasks: Clustering, Dimensionality Reduction, General Classification

We introduce, test and discuss a method for classifying and clustering data
modeled as directed graphs. The idea is to start diffusion processes from any
subset of a data collection, generating corresponding distributions for
reaching points in the network. These distributions take the form of
high-dimensional numerical vectors and capture essential topological properties
of the original dataset. We show how these diffusion vectors can be
successfully applied for getting state-of-the-art accuracies in the problem of
extracting pathways from metabolic networks. We also provide a guideline to
illustrate how to use our method for classification problems, and discuss
important details of its implementation. In particular, we present a simple
dimensionality reduction technique that lowers the computational cost of
classifying diffusion vectors, while leaving the predictive power of the
classification process substantially unaltered. Although the method has very
few parameters, the results we obtain show its flexibility and power. This
should make it helpful in many other contexts.
How to improve robustness in Kohonen maps and display additional information in Factorial Analysis: application to text mining
Authors: Nicolas Bourgeois, Marie Cottrell, Benjamin Déruelle, Stéphane Lamassé, Patrick Letrémy
Date: 2015-06-25 00:00:00

This article is an extended version of a paper presented in the WSOM'2012
conference [1]. We display a combination of factorial projections, SOM
algorithm and graph techniques applied to a text mining problem. The corpus
contains 8 medieval manuscripts which were used to teach arithmetic techniques
to merchants. Among the techniques for Data Analysis, those used for
Lexicometry (such as Factorial Analysis) highlight the discrepancies between
manuscripts. The reason for this is that they focus on the deviation from the
independence between words and manuscripts. Still, we also want to discover and
characterize the common vocabulary among the whole corpus. Using the properties
of stochastic Kohonen maps, which define neighborhood between inputs in a
non-deterministic way, we highlight the words which seem to play a special role
in the vocabulary. We call them fickle and use them to improve both Kohonen map
robustness and significance of FCA visualization. Finally we use graph
algorithmic to exploit this fickleness for classification of words.
Fairness-Aware Learning with Restriction of Universal Dependency using f-Divergences
Authors: Kazuto Fukuchi, Jun Sakuma
Date: 2015-06-25 00:00:00
Tasks: Fairness

Fairness-aware learning is a novel framework for classification tasks. Like
regular empirical risk minimization (ERM), it aims to learn a classifier with a
low error rate, and at the same time, for the predictions of the classifier to
be independent of sensitive features, such as gender, religion, race, and
ethnicity. Existing methods can achieve low dependencies on given samples, but
this is not guaranteed on unseen samples. The existing fairness-aware learning
algorithms employ different dependency measures, and each algorithm is
specifically designed for a particular one. Such diversity makes it difficult
to theoretically analyze and compare them. In this paper, we propose a general
framework for fairness-aware learning that uses f-divergences and that covers
most of the dependency measures employed in the existing methods. We introduce
a way to estimate the f-divergences that allows us to give a unified analysis
for the upper bound of the estimation error; this bound is tighter than that of
the existing convergence rate analysis of the divergence estimation. With our
divergence estimate, we propose a fairness-aware learning algorithm, and
perform a theoretical analysis of its generalization error. Our analysis
reveals that, under mild assumptions and even with enforcement of fairness, the
generalization error of our method is $O(\sqrt{1/n})$, which is the same as
that of the regular ERM. In addition, and more importantly, we show that, for
any f-divergence, the upper bound of the estimation error of the divergence is
$O(\sqrt{1/n})$. This indicates that our fairness-aware learning algorithm
guarantees low dependencies on unseen samples for any dependency measure
represented by an f-divergence.
Role of normalization in spectral clustering for stochastic blockmodels
Authors: Purnamrita Sarkar, Peter J. Bickel
Date: 2013-10-05 00:00:00
Tasks: Clustering

Spectral clustering is a technique that clusters elements using the top few
eigenvectors of their (possibly normalized) similarity matrix. The quality of
spectral clustering is closely tied to the convergence properties of these
principal eigenvectors. This rate of convergence has been shown to be identical
for both the normalized and unnormalized variants in recent random matrix
theory literature. However, normalization for spectral clustering is commonly
believed to be beneficial [Stat. Comput. 17 (2007) 395-416]. Indeed, our
experiments show that normalization improves prediction accuracy. In this
paper, for the popular stochastic blockmodel, we theoretically show that
normalization shrinks the spread of points in a class by a constant fraction
under a broad parameter regime. As a byproduct of our work, we also obtain
sharp deviation bounds of empirical principal eigenvalues of graphs generated
from a stochastic blockmodel.
Manifold Optimization for Gaussian Mixture Models
Authors: Reshad Hosseini, Suvrit Sra
Date: 2015-06-25 00:00:00
Tasks: Density Estimation, parameter estimation, Riemannian optimization

We take a new look at parameter estimation for Gaussian Mixture Models
(GMMs). In particular, we propose using \emph{Riemannian manifold optimization}
as a powerful counterpart to Expectation Maximization (EM). An out-of-the-box
invocation of manifold optimization, however, fails spectacularly: it converges
to the same solution but vastly slower. Driven by intuition from manifold
convexity, we then propose a reparamerization that has remarkable empirical
consequences. It makes manifold optimization not only match EM---a highly
encouraging result in itself given the poor record nonlinear programming
methods have had against EM so far---but also outperform EM in many practical
settings, while displaying much less variability in running times. We further
highlight the strengths of manifold optimization by developing a somewhat tuned
manifold LBFGS method that proves even more competitive and reliable than
existing manifold optimization tools. We hope that our results encourage a
wider consideration of manifold optimization for parameter estimation problems.
Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling
Authors: Kun Xu, Yansong Feng, Songfang Huang, Dongyan Zhao
Date: 2015-06-25 00:00:00
Proceeding: EMNLP 2015 9
Tasks: General Classification, Relation, Relation Classification, Sentence

Syntactic features play an essential role in identifying relationship in a
sentence. Previous neural network models often suffer from irrelevant
information introduced when subjects and objects are in a long distance. In
this paper, we propose to learn more robust relation representations from the
shortest dependency path through a convolution neural network. We further
propose a straightforward negative sampling strategy to improve the assignment
of subjects and objects. Experimental results show that our method outperforms
the state-of-the-art methods on the SemEval-2010 Task 8 dataset.
A PTAS for Agnostically Learning Halfspaces
Authors: Amit Daniely
Date: 2014-10-26 00:00:00
Tasks: regression

We present a PTAS for agnostically learning halfspaces w.r.t. the uniform
distribution on the $d$ dimensional sphere. Namely, we show that for every
$\mu>0$ there is an algorithm that runs in time
$\mathrm{poly}(d,\frac{1}{\epsilon})$, and is guaranteed to return a classifier
with error at most $(1+\mu)\mathrm{opt}+\epsilon$, where $\mathrm{opt}$ is the
error of the best halfspace classifier. This improves on Awasthi, Balcan and
Long [ABL14] who showed an algorithm with an (unspecified) constant
approximation ratio. Our algorithm combines the classical technique of
polynomial regression (e.g. [LMN89, KKMS05]), together with the new
localization technique of [ABL14].
Joint community and anomaly tracking in dynamic networks
Authors: Brian Baingana, Georgios B. Giannakis
Date: 2015-06-25 00:00:00
Tasks: Community Detection, Time Series, Time Series Analysis

Most real-world networks exhibit community structure, a phenomenon
characterized by existence of node clusters whose intra-edge connectivity is
stronger than edge connectivities between nodes belonging to different
clusters. In addition to facilitating a better understanding of network
behavior, community detection finds many practical applications in diverse
settings. Communities in online social networks are indicative of shared
functional roles, or affiliation to a common socio-economic status, the
knowledge of which is vital for targeted advertisement. In buyer-seller
networks, community detection facilitates better product recommendations.
Unfortunately, reliability of community assignments is hindered by anomalous
user behavior often observed as unfair self-promotion, or "fake"
highly-connected accounts created to promote fraud. The present paper advocates
a novel approach for jointly tracking communities while detecting such
anomalous nodes in time-varying networks. By postulating edge creation as the
result of mutual community participation by node pairs, a dynamic factor model
with anomalous memberships captured through a sparse outlier matrix is put
forth. Efficient tracking algorithms suitable for both online and decentralized
operation are developed. Experiments conducted on both synthetic and real
network time series successfully unveil underlying communities and anomalous
nodes.
CRAFT: ClusteR-specific Assorted Feature selecTion
Authors: Vikas K. Garg, Cynthia Rudin, Tommi Jaakkola
Date: 2015-06-25 00:00:00
Tasks: Clustering, feature selection

We present a framework for clustering with cluster-specific feature
selection. The framework, CRAFT, is derived from asymptotic log posterior
formulations of nonparametric MAP-based clustering models. CRAFT handles
assorted data, i.e., both numeric and categorical data, and the underlying
objective functions are intuitively appealing. The resulting algorithm is
simple to implement and scales nicely, requires minimal parameter tuning,
obviates the need to specify the number of clusters a priori, and compares
favorably with other methods on real datasets.
Incremental RANSAC for Online Relocation in Large Dynamic Environments
Authors: Kanji Tanaka, Eiji Kondo
Date: 2015-06-24 00:00:00
Tasks: Position

Vehicle relocation is the problem in which a mobile robot has to estimate the
self-position with respect to an a priori map of landmarks using the perception
and the motion measurements without using any knowledge of the initial
self-position. Recently, RANdom SAmple Consensus (RANSAC), a robust
multi-hypothesis estimator, has been successfully applied to offline relocation
in static environments. On the other hand, online relocation in dynamic
environments is still a difficult problem, for available computation time is
always limited, and for measurement include many outliers. To realize real time
algorithm for such an online process, we have developed an incremental version
of RANSAC algorithm by extending an efficient preemption RANSAC scheme. This
novel scheme named incremental RANSAC is able to find inlier hypotheses of
self-positions out of large number of outlier hypotheses contaminated by
outlier measurements.
An objective prior that unifies objective Bayes and information-based inference
Authors: Colin H. LaMont, Paul A. Wiggins
Date: 2015-06-02 00:00:00

There are three principle paradigms of statistical inference: (i) Bayesian,
(ii) information-based and (iii) frequentist inference. We describe an
objective prior (the weighting or $w$-prior) which unifies objective Bayes and
information-based inference. The $w$-prior is chosen to make the marginal
probability an unbiased estimator of the predictive performance of the model.
This definition has several other natural interpretations. From the perspective
of the information content of the prior, the $w$-prior is both uniformly and
maximally uninformative. The $w$-prior can also be understood to result in a
uniform density of distinguishable models in parameter space. Finally we
demonstrate the the $w$-prior is equivalent to the Akaike Information Criterion
(AIC) for regular models in the asymptotic limit. The $w$-prior appears to be
generically applicable to statistical inference and is free of {\it ad hoc}
regularization. The mechanism for suppressing complexity is analogous to AIC:
model complexity reduces model predictivity. We expect this new objective-Bayes
approach to inference to be widely-applicable to machine-learning problems
including singular models.
Degenerate Motions in Multicamera Cluster SLAM with Non-overlapping Fields of View
Authors: Michael J. Tribou, David W. L. Wang, Steven L. Waslander
Date: 2015-06-25 00:00:00
Tasks: Simultaneous Localization and Mapping

An analysis of the relative motion and point feature model configurations
leading to solution degeneracy is presented, for the case of a Simultaneous
Localization and Mapping system using multicamera clusters with non-overlapping
fields-of-view. The SLAM optimization system seeks to minimize image space
reprojection error and is formulated for a cluster containing any number of
component cameras, observing any number of point features over two keyframes.
The measurement Jacobian is transformed to expose a reduced-dimension
representation such that the degeneracy of the system can be determined by the
rank of a dense submatrix. A set of relative motions sufficient for degeneracy
are identified for certain cluster configurations, independent of target model
geometry. Furthermore, it is shown that increasing the number of cameras within
the cluster and observing features across different cameras over the two
keyframes reduces the size of the degenerate motion sets significantly.
Multiresolution Approach to Acceleration of Iterative Image Reconstruction for X-Ray Imaging for Security Applications
Authors: S. Degirmenci, Joseph A. O'Sullivan, David G. Politte
Date: 2015-06-24 00:00:00
Tasks: Image Reconstruction

Three-dimensional x-ray CT image reconstruction in baggage scanning in
security applications is an important research field. The variety of materials
to be reconstructed is broader than medical x-ray imaging. Presence of high
attenuating materials such as metal may cause artifacts if analytical
reconstruction methods are used. Statistical modeling and the resultant
iterative algorithms are known to reduce these artifacts and present good
quantitative accuracy in estimates of linear attenuation coefficients. However,
iterative algorithms may require computations in order to achieve
quantitatively accurate results. For the case of baggage scanning, in order to
provide fast accurate inspection throughput, they must be accelerated
drastically. There are many approaches proposed in the literature to increase
speed of convergence. This paper presents a new method that estimates the
wavelet coefficients of the images in the discrete wavelet transform domain
instead of the image space itself. Initially, surrogate functions are created
around approximation coefficients only. As the iterations proceed, the wavelet
tree on which the updates are made is expanded based on a criterion and detail
coefficients at each level are updated and the tree is expanded this way. For
example, in the smooth regions of the image the detail coefficients are not
updated while the coefficients that represent the high-frequency component
around edges are being updated, thus saving time by focusing computations where
they are needed. This approach is implemented on real data from a SureScan (TM)
x1000 Explosive Detection System and compared to straightforward implementation
of the unregularized alternating minimization of O'Sullivan and Benac [1].
CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits
Authors: Mohammad Norouzi, Maxwell D. Collins, David J. Fleet, Pushmeet Kohli
Date: 2015-06-19 00:00:00
Tasks: General Classification, Multi-class Classification

We propose a novel algorithm for optimizing multivariate linear threshold
functions as split functions of decision trees to create improved Random Forest
classifiers. Standard tree induction methods resort to sampling and exhaustive
search to find good univariate split functions. In contrast, our method
computes a linear combination of the features at each node, and optimizes the
parameters of the linear combination (oblique) split functions by adopting a
variant of latent variable SVM formulation. We develop a convex-concave upper
bound on the classification loss for a one-level decision tree, and optimize
the bound by stochastic gradient descent at each internal node of the tree.
Forests of up to 1000 Continuously Optimized Oblique (CO2) decision trees are
created, which significantly outperform Random Forest with univariate splits
and previous techniques for constructing oblique trees. Experimental results
are reported on multi-class classification benchmarks and on Labeled Faces in
the Wild (LFW) dataset.
Learning Representations from Deep Networks Using Mode Synthesizers
Authors: N. E. Osegi, P. Enyindah
Date: 2015-06-24 00:00:00
Tasks: Deep Learning

Deep learning Networks play a crucial role in the evolution of a vast number
of current machine learning models for solving a variety of real world
non-trivial tasks. Such networks use big data which is generally unlabeled
unsupervised and multi-layered requiring no form of supervision for training
and learning data and has been used to successfully build automatic supervisory
neural networks. However the question still remains how well the learned data
represents interestingness, and their effectiveness i.e. efficiency in deep
learning models or applications. If the output of a network of deep learning
models can be beamed unto a scene of observables, we could learn the
variational frequencies of these stacked networks in a parallel and
distributive way.This paper seeks to discover and represent interesting
patterns in an efficient and less complex way by incorporating the concept of
Mode synthesizers in the deep learning process models
Global Optimality in Tensor Factorization, Deep Learning, and Beyond
Authors: Benjamin D. Haeffele, Rene Vidal
Date: 2015-06-24 00:00:00
Tasks: Deep Learning

Techniques involving factorization are found in a wide range of applications
and have enjoyed significant empirical success in many fields. However, common
to a vast majority of these problems is the significant disadvantage that the
associated optimization problems are typically non-convex due to a multilinear
form or other convexity destroying transformation. Here we build on ideas from
convex relaxations of matrix factorizations and present a very general
framework which allows for the analysis of a wide range of non-convex
factorization problems - including matrix factorization, tensor factorization,
and deep neural network training formulations. We derive sufficient conditions
to guarantee that a local minimum of the non-convex optimization problem is a
global minimum and show that if the size of the factorized variables is large
enough then from any initialization it is possible to find a global minimizer
using a purely local descent algorithm. Our framework also provides a partial
theoretical justification for the increasingly common use of Rectified Linear
Units (ReLUs) in deep neural networks and offers guidance on deep network
architectures and regularization strategies to facilitate efficient
optimization.