pwc-archive/papers-with-abstracts huggingface dataset:
A Hierarchical Spectral Method for Extreme Classification
Authors: Paul Mineiro, Nikos Karampatziakis
Date: 2015-11-10 00:00:00
Tasks: Classification, General Classification, Tree Decomposition

Extreme classification problems are multiclass and multilabel classification
problems where the number of outputs is so large that straightforward
strategies are neither statistically nor computationally viable. One strategy
for dealing with the computational burden is via a tree decomposition of the
output space. While this typically leads to training and inference that scales
sublinearly with the number of outputs, it also results in reduced statistical
performance. In this work, we identify two shortcomings of tree decomposition
methods, and describe two heuristic mitigations. We compose these with an
eigenvalue technique for constructing the tree. The end result is a
computationally efficient algorithm that provides good statistical performance
on several extreme data sets.
A study on tuning parameter selection for the high-dimensional lasso
Authors: Darren Homrighausen, Daniel J. McDonald
Date: 2016-02-04 00:00:00
Tasks: Vocal Bursts Intensity Prediction

High-dimensional predictive models, those with more measurements than observations, require regularization to be well defined, perform well empirically, and possess theoretical guarantees. The amount of regularization, often determined by tuning parameters, is integral to achieving good performance. One can choose the tuning parameter in a variety of ways, such as through resampling methods or generalized information criteria. However, the theory supporting many regularized procedures relies on an estimate for the variance parameter, which is complicated in high dimensions. We develop a suite of information criteria for choosing the tuning parameter in lasso regression by leveraging the literature on high-dimensional variance estimation. We derive intuition showing that existing information-theoretic approaches work poorly in this setting. We compare our risk estimators to existing methods with an extensive simulation and derive some theoretical justification. We find that our new estimators perform well across a wide range of simulation conditions and evaluation criteria.
Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification
Authors: Keiller Nogueira, Otávio A. B. Penatti, Jefersson A. dos Santos
Date: 2016-02-04 00:00:00
Tasks: General Classification, Scene Classification

We present an analysis of three possible strategies for exploiting the power
of existing convolutional neural networks (ConvNets) in different scenarios
from the ones they were trained: full training, fine tuning, and using ConvNets
as feature extractors. In many applications, especially including remote
sensing, it is not feasible to fully design and train a new ConvNet, as this
usually requires a considerable amount of labeled data and demands high
computational costs. Therefore, it is important to understand how to obtain the
best profit from existing ConvNets. We perform experiments with six popular
ConvNets using three remote sensing datasets. We also compare ConvNets in each
strategy with existing descriptors and with state-of-the-art baselines. Results
point that fine tuning tends to be the best performing strategy. In fact, using
the features from the fine-tuned ConvNet with linear SVM obtains the best
results. We also achieved state-of-the-art results for the three datasets used.
Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object Recognition
Authors: Priyadarshini Panda, Kaushik Roy
Date: 2016-02-03 00:00:00
Tasks: Decoder, General Classification, Object Recognition

We present a spike-based unsupervised regenerative learning scheme to train
Spiking Deep Networks (SpikeCNN) for object recognition problems using
biologically realistic leaky integrate-and-fire neurons. The training
methodology is based on the Auto-Encoder learning model wherein the
hierarchical network is trained layer wise using the encoder-decoder principle.
Regenerative learning uses spike-timing information and inherent latencies to
update the weights and learn representative levels for each convolutional layer
in an unsupervised manner. The features learnt from the final layer in the
hierarchy are then fed to an output layer. The output layer is trained with
supervision by showing a fraction of the labeled training dataset and performs
the overall classification of the input. Our proposed methodology yields
0.92%/29.84% classification error on MNIST/CIFAR10 datasets which is comparable
with state-of-the-art results. The proposed methodology also introduces
sparsity in the hierarchical feature representations on account of event-based
coding resulting in computationally efficient learning.
Efficient statistical classification of satellite measurements
Authors: Peter Mills
Date: 2012-02-10 00:00:00
Tasks: Classification, General Classification, Quantization

Supervised statistical classification is a vital tool for satellite image
processing. It is useful not only when a discrete result, such as feature
extraction or surface type, is required, but also for continuum retrievals by
dividing the quantity of interest into discrete ranges. Because of the high
resolution of modern satellite instruments and because of the requirement for
real-time processing, any algorithm has to be fast to be useful. Here we
describe an algorithm based on kernel estimation called Adaptive Gaussian
Filtering that incorporates several innovations to produce superior efficiency
as compared to three other popular methods: k-nearest-neighbour (KNN), Learning
Vector Quantization (LVQ) and Support Vector Machines (SVM). This efficiency is
gained with no compromises: accuracy is maintained, while estimates of the
conditional probabilities are returned. These are useful not only to gauge the
accuracy of an estimate in the absence of its true value, but also to
re-calibrate a retrieved image and as a proxy for a discretized continuum
variable. The algorithm is demonstrated and compared with the other three on a
pair of synthetic test classes and to map the waterways of the Netherlands.
Software may be found at: http://libagf.sourceforge.net.
Fast Cross-Validation via Sequential Testing
Authors: Tammo Krueger, Danny Panknin, Mikio Braun
Date: 2012-06-11 00:00:00
Tasks: Model Selection

With the increasing size of today's data sets, finding the right parameter
configuration in model selection via cross-validation can be an extremely
time-consuming task. In this paper we propose an improved cross-validation
procedure which uses nonparametric testing coupled with sequential analysis to
determine the best parameter set on linearly increasing subsets of the data. By
eliminating underperforming candidates quickly and keeping promising candidates
as long as possible, the method speeds up the computation while preserving the
capability of the full cross-validation. Theoretical considerations underline
the statistical power of our procedure. The experimental evaluation shows that
our method reduces the computation time by a factor of up to 120 compared to a
full cross-validation with a negligible impact on the accuracy.
Latent-Class Hough Forests for 6 DoF Object Pose Estimation
Authors: Rigas Kouskouridas, Alykhan Tejani, Andreas Doumanoglou, Danhang Tang, Tae-Kyun Kim
Date: 2016-02-03 00:00:00
Tasks: object-detection, Object Detection, Pose Estimation, Template Matching

In this paper we present Latent-Class Hough Forests, a method for object
detection and 6 DoF pose estimation in heavily cluttered and occluded
scenarios. We adapt a state of the art template matching feature into a
scale-invariant patch descriptor and integrate it into a regression forest
using a novel template-based split function. We train with positive samples
only and we treat class distributions at the leaf nodes as latent variables.
During testing we infer by iteratively updating these distributions, providing
accurate estimation of background clutter and foreground occlusions and, thus,
better detection rate. Furthermore, as a by-product, our Latent-Class Hough
Forests can provide accurate occlusion aware segmentation masks, even in the
multi-instance scenario. In addition to an existing public dataset, which
contains only single-instance sequences with large amounts of clutter, we have
collected two, more challenging, datasets for multiple-instance detection
containing heavy 2D and 3D clutter as well as foreground occlusions. We provide
extensive experiments on the various parameters of the framework such as patch
size, number of trees and number of iterations to infer class distributions at
test time. We also evaluate the Latent-Class Hough Forests on all datasets
where we outperform state of the art methods.
"Draw My Topics": Find Desired Topics fast from large scale of Corpus
Authors: Jason Dou, Ni Sun, Xiaojun Zou
Date: 2016-02-03 00:00:00
Tasks: Topic Models

We develop the "Draw My Topics" toolkit, which provides a fast way to
incorporate social scientists' interest into standard topic modelling. Instead
of using raw corpus with primitive processing as input, an algorithm based on
Vector Space Model and Conditional Entropy are used to connect social
scientists' willingness and unsupervised topic models' output. Space for users'
adjustment on specific corpus of their interest is also accommodated. We
demonstrate the toolkit's use on the Diachronic People's Daily Corpus in
Chinese.
Shearlet-Based Detection of Flame Fronts
Authors: Rafael Reisenhofer, Johannes Kiefer, Emily J. King
Date: 2015-11-12 00:00:00
Tasks: Line Detection

Identifying and characterizing flame fronts is the most common task in the
computer-assisted analysis of data obtained from imaging techniques such as
planar laser-induced fluorescence (PLIF), laser Rayleigh scattering (LRS), or
particle imaging velocimetry (PIV). We present a novel edge and ridge (line)
detection algorithm based on complex-valued wavelet-like analyzing functions --
so-called complex shearlets -- displaying several traits useful for the
extraction of flame fronts. In addition to providing a unified approach to the
detection of edges and ridges, our method inherently yields estimates of local
tangent orientations and local curvatures. To examine the applicability for
high-frequency recordings of combustion processes, the algorithm is applied to
mock images distorted with varying degrees of noise and real-world PLIF images
of both OH and CH radicals. Furthermore, we compare the performance of the
newly proposed complex shearlet-based measure to well-established edge and
ridge detection techniques such as the Canny edge detector, another
shearlet-based edge detector, and the phase congruency measure.
Finding the different patterns in buildings data using bag of words representation with clustering
Authors: Usman Habib, Gerhard Zucker
Date: 2016-02-03 00:00:00
Tasks: Clustering, Dynamic Time Warping

The understanding of the buildings operation has become a challenging task
due to the large amount of data recorded in energy efficient buildings. Still,
today the experts use visual tools for analyzing the data. In order to make the
task realistic, a method has been proposed in this paper to automatically
detect the different patterns in buildings. The K Means clustering is used to
automatically identify the ON (operational) cycles of the chiller. In the next
step the ON cycles are transformed to symbolic representation by using Symbolic
Aggregate Approximation (SAX) method. Then the SAX symbols are converted to bag
of words representation for hierarchical clustering. Moreover, the proposed
technique is applied to real life data of adsorption chiller. Additionally, the
results from the proposed method and dynamic time warping (DTW) approach are
also discussed and compared.
Fast spectral algorithms from sum-of-squares proofs: tensor decomposition and planted sparse vectors
Authors: Samuel B. Hopkins, Tselil Schramm, Jonathan Shi, David Steurer
Date: 2015-12-08 00:00:00
Tasks: Tensor Decomposition

We consider two problems that arise in machine learning applications: the
problem of recovering a planted sparse vector in a random linear subspace and
the problem of decomposing a random low-rank overcomplete 3-tensor. For both
problems, the best known guarantees are based on the sum-of-squares method. We
develop new algorithms inspired by analyses of the sum-of-squares method. Our
algorithms achieve the same or similar guarantees as sum-of-squares for these
problems but the running time is significantly faster.
  For the planted sparse vector problem, we give an algorithm with running time
nearly linear in the input size that approximately recovers a planted sparse
vector with up to constant relative sparsity in a random subspace of $\mathbb
R^n$ of dimension up to $\tilde \Omega(\sqrt n)$. These recovery guarantees
match the best known ones of Barak, Kelner, and Steurer (STOC 2014) up to
logarithmic factors.
  For tensor decomposition, we give an algorithm with running time close to
linear in the input size (with exponent $\approx 1.086$) that approximately
recovers a component of a random 3-tensor over $\mathbb R^n$ of rank up to
$\tilde \Omega(n^{4/3})$. The best previous algorithm for this problem due to
Ge and Ma (RANDOM 2015) works up to rank $\tilde \Omega(n^{3/2})$ but requires
quasipolynomial time.
Biclustering Readings and Manuscripts via Non-negative Matrix Factorization, with Application to the Text of Jude
Authors: Joey McCollum, Stephen Brown
Date: 2016-02-03 00:00:00

The text-critical practice of grouping witnesses into families or texttypes
often faces two obstacles: Contamination in the manuscript tradition, and
co-dependence in identifying characteristic readings and manuscripts. We
introduce non-negative matrix factorization (NMF) as a simple, unsupervised,
and efficient way to cluster large numbers of manuscripts and readings
simultaneously while summarizing contamination using an easy-to-interpret
mixture model. We apply this method to an extensive collation of the New
Testament epistle of Jude and show that the resulting clusters correspond to
human-identified textual families from existing research.
A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks
Authors: Luke B. Godfrey, Michael S. Gashler
Date: 2016-02-03 00:00:00

We present the soft exponential activation function for artificial neural
networks that continuously interpolates between logarithmic, linear, and
exponential functions. This activation function is simple, differentiable, and
parameterized so that it can be trained as the rest of the network is trained.
We hypothesize that soft exponential has the potential to improve neural
network learning, as it can exactly calculate many natural operations that
typical neural networks can only approximate, including addition,
multiplication, inner product, distance, polynomials, and sinusoids.
Diffusion Maximum Correntropy Criterion Algorithms for Robust Distributed Estimation
Authors: Wentao Ma, Badong Chen, Jiandong Duan, Haiquan Zhao
Date: 2015-08-08 00:00:00

Robust diffusion adaptive estimation algorithms based on the maximum
correntropy criterion (MCC), including adaptation to combination MCC and
combination to adaptation MCC, are developed to deal with the distributed
estimation over network in impulsive (long-tailed) noise environments. The cost
functions used in distributed estimation are in general based on the mean
square error (MSE) criterion, which is desirable when the measurement noise is
Gaussian. In non-Gaussian situations, such as the impulsive-noise case, MCC
based methods may achieve much better performance than the MSE methods as they
take into account higher order statistics of error distribution. The proposed
methods can also outperform the robust diffusion least mean p-power(DLMP) and
diffusion minimum error entropy (DMEE) algorithms. The mean and mean square
convergence analysis of the new algorithms are also carried out.
Multiple Output Regression with Latent Noise
Authors: Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Mehreen Ali, Aki S. Havulinna, Marjo-Riitta Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski
Date: 2014-10-27 00:00:00
Tasks: regression, Time Series, Time Series Analysis

In high-dimensional data, structured noise caused by observed and unobserved
factors affecting multiple target variables simultaneously, imposes a serious
challenge for modeling, by masking the often weak signal. Therefore, (1)
explaining away the structured noise in multiple-output regression is of
paramount importance. Additionally, (2) assumptions about the correlation
structure of the regression weights are needed. We note that both can be
formulated in a natural way in a latent variable model, in which both the
interesting signal and the noise are mediated through the same latent factors.
Under this assumption, the signal model then borrows strength from the noise
model by encouraging similar effects on correlated targets. We introduce a
hyperparameter for the \emph{latent signal-to-noise ratio} which turns out to
be important for modelling weak signals, and an ordered infinite-dimensional
shrinkage prior that resolves the rotational unidentifiability in reduced-rank
regression models. Simulations and prediction experiments with metabolite, gene
expression, FMRI measurement, and macroeconomic time series data show that our
model equals or exceeds the state-of-the-art performance and, in particular,
outperforms the standard approach of assuming independent noise and signal
models.
Using Hadoop for Large Scale Analysis on Twitter: A Technical Report
Authors: Nikolaos Nodarakis, Spyros Sioutas, Athanasios Tsakalidis, Giannis Tzimas
Date: 2016-02-03 00:00:00
Tasks: Decision Making, Opinion Mining, Sentiment Analysis

Sentiment analysis (or opinion mining) on Twitter data has attracted much
attention recently. One of the system's key features, is the immediacy in
communication with other users in an easy, user-friendly and fast way.
Consequently, people tend to express their feelings freely, which makes Twitter
an ideal source for accumulating a vast amount of opinions towards a wide
diversity of topics. This amount of information offers huge potential and can
be harnessed to receive the sentiment tendency towards these topics. However,
since none can invest an infinite amount of time to read through these tweets,
an automated decision making approach is necessary. Nevertheless, most existing
solutions are limited in centralized environments only. Thus, they can only
process at most a few thousand tweets. Such a sample, is not representative to
define the sentiment polarity towards a topic due to the massive number of
tweets published daily. In this paper, we go one step further and develop a
novel method for sentiment learning in the MapReduce framework. Our algorithm
exploits the hashtags and emoticons inside a tweet, as sentiment labels, and
proceeds to a classification procedure of diverse sentiment types in a parallel
and distributed manner. Moreover, we utilize Bloom filters to compact the
storage size of intermediate data and boost the performance of our algorithm.
Through an extensive experimental evaluation, we prove that our solution is
efficient, robust and scalable and confirm the quality of our sentiment
identification.
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
Authors: Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos, Ioannis Kompatsiaris
Date: 2016-02-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG), feature selection, SSVEP

Brain-computer interfaces (BCIs) have been gaining momentum in making
human-computer interaction more natural, especially for people with
neuro-muscular disabilities. Among the existing solutions the systems relying
on electroencephalograms (EEG) occupy the most prominent place due to their
non-invasiveness. However, the process of translating EEG signals into computer
commands is far from trivial, since it requires the optimization of many
different parameters that need to be tuned jointly. In this report, we focus on
the category of EEG-based BCIs that rely on Steady-State-Visual-Evoked
Potentials (SSVEPs) and perform a comparative evaluation of the most promising
algorithms existing in the literature. More specifically, we define a set of
algorithms for each of the various different parameters composing a BCI system
(i.e. filtering, artifact removal, feature extraction, feature selection and
classification) and study each parameter independently by keeping all other
parameters fixed. The results obtained from this evaluation process are
provided together with a dataset consisting of the 256-channel, EEG signals of
11 subjects, as well as a processing toolbox for reproducing the results and
supporting further experimentation. In this way, we manage to make available
for the community a state-of-the-art baseline for SSVEP-based BCIs that can be
used as a basis for introducing novel methods and approaches.
Consistency of Spectral Hypergraph Partitioning under Planted Partition Model
Authors: Debarghya Ghoshdastidar, Ambedkar Dukkipati
Date: 2015-05-07 00:00:00
Tasks: graph partitioning, hypergraph partitioning, Stochastic Block Model

Hypergraph partitioning lies at the heart of a number of problems in machine
learning and network sciences. Many algorithms for hypergraph partitioning have
been proposed that extend standard approaches for graph partitioning to the
case of hypergraphs. However, theoretical aspects of such methods have seldom
received attention in the literature as compared to the extensive studies on
the guarantees of graph partitioning. For instance, consistency results of
spectral graph partitioning under the stochastic block model are well known. In
this paper, we present a planted partition model for sparse random non-uniform
hypergraphs that generalizes the stochastic block model. We derive an error
bound for a spectral hypergraph partitioning algorithm under this model using
matrix concentration inequalities. To the best of our knowledge, this is the
first consistency result related to partitioning non-uniform hypergraphs.
Image and Information
Authors: Frank Nielsen
Date: 2016-02-03 00:00:00

A well-known old adage says that {\em "A picture is worth a thousand words!"}
(attributed to the Chinese philosopher Confucius ca 500 years BC). But more
precisely, what do we mean by information in images? And how can it be
retrieved effectively by machines? We briefly highlight these puzzling
questions in this column. But first of all, let us start by defining more
precisely what is meant by an "Image."
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4
Authors: Cong Yao, Jia-Nan Wu, Xinyu Zhou, Chi Zhang, Shuchang Zhou, Zhimin Cao, Qi Yin
Date: 2015-11-30 00:00:00
Tasks: Diversity, Scene Text Detection, Text Detection

Different from focused texts present in natural images, which are captured
with user's intention and intervention, incidental texts usually exhibit much
more diversity, variability and complexity, thus posing significant
difficulties and challenges for scene text detection and recognition
algorithms. The ICDAR 2015 Robust Reading Competition Challenge 4 was launched
to assess the performance of existing scene text detection and recognition
methods on incidental texts as well as to stimulate novel ideas and solutions.
This report is dedicated to briefly introduce our strategies for this
challenging problem and compare them with prior arts in this field.