pwc-archive/papers-with-abstracts huggingface dataset:
Image Fusion Techniques in Remote Sensing
Authors: Reham Gharbia, Ahmad Taher Azar, Ali El Baz, Aboul Ella Hassanien
Date: 2014-03-11 00:00:00

Remote sensing image fusion is an effective way to use a large volume of data
from multisensor images. Most earth satellites such as SPOT, Landsat 7, IKONOS
and QuickBird provide both panchromatic (Pan) images at a higher spatial
resolution and multispectral (MS) images at a lower spatial resolution and many
remote sensing applications require both high spatial and high spectral
resolutions, especially for GIS based applications. An effective image fusion
technique can produce such remotely sensed images. Image fusion is the
combination of two or more different images to form a new image by using a
certain algorithm to obtain more and better information about an object or a
study area than. The image fusion is performed at three different processing
levels which are pixel level, feature level and decision level according to the
stage at which the fusion takes place. There are many image fusion methods that
can be used to produce high resolution multispectral images from a high
resolution pan image and low resolution multispectral images. This paper
explores the major remote sensing data fusion techniques at pixel level and
reviews the concept, principals, limitations and advantages for each technique.
This paper focused on traditional techniques like intensity hue-saturation-
(HIS), Brovey, principal component analysis (PCA) and Wavelet.
Generalised Mixability, Constant Regret, and Bayesian Updating
Authors: Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson
Date: 2014-03-10 00:00:00

Mixability of a loss is known to characterise when constant regret bounds are
achievable in games of prediction with expert advice through the use of Vovk's
aggregating algorithm. We provide a new interpretation of mixability via convex
analysis that highlights the role of the Kullback-Leibler divergence in its
definition. This naturally generalises to what we call $\Phi$-mixability where
the Bregman divergence $D_\Phi$ replaces the KL divergence. We prove that
losses that are $\Phi$-mixable also enjoy constant regret bounds via a
generalised aggregating algorithm that is similar to mirror descent.
Generic Deep Networks with Wavelet Scattering
Authors: Edouard Oyallon, Stéphane Mallat, Laurent SIfre
Date: 2013-12-20 00:00:00
Tasks: General Classification

We introduce a two-layer wavelet scattering network, for object
classification. This scattering transform computes a spatial wavelet transform
on the first layer and a new joint wavelet transform along spatial, angular and
scale variables in the second layer. Numerical experiments demonstrate that
this two layer convolution network, which involves no learning and no max
pooling, performs efficiently on complex image data sets such as CalTech, with
structural objects variability and clutter. It opens the possibility to
simplify deep neural network learning by initializing the first layers with
wavelet filters.
Phase Retrieval using Lipschitz Continuous Maps
Authors: Radu Balan, Dongmian Zou
Date: 2014-03-10 00:00:00
Tasks: Retrieval

In this note we prove that reconstruction from magnitudes of frame
coefficients (the so called "phase retrieval problem") can be performed using
Lipschitz continuous maps. Specifically we show that when the nonlinear
analysis map $\alpha:{\mathcal H}\rightarrow\mathbb{R}^m$ is injective, with
$(\alpha(x))_k=|<x,f_k>|^2$, where $\{f_1,\ldots,f_m\}$ is a frame for the
Hilbert space ${\mathcal H}$, then there exists a left inverse map
$\omega:\mathbb{R}^m\rightarrow {\mathcal H}$ that is Lipschitz continuous.
Additionally we obtain the Lipschitz constant of this inverse map in terms of
the lower Lipschitz constant of $\alpha$. Surprisingly the increase in
Lipschitz constant is independent of the space dimension or frame redundancy.
Sublinear Models for Graphs
Authors: Brijnesh J. Jain
Date: 2014-03-10 00:00:00

This contribution extends linear models for feature vectors to sublinear
models for graphs and analyzes their properties. The results are (i) a
geometric interpretation of sublinear classifiers, (ii) a generic learning rule
based on the principle of empirical risk minimization, (iii) a convergence
theorem for the margin perceptron in the sublinearly separable case, and (iv)
the VC-dimension of sublinear functions. Empirical results on graph data show
that sublinear models on graphs have similar properties as linear models for
feature vectors.
Dropout improves Recurrent Neural Networks for Handwriting Recognition
Authors: Vu Pham, Théodore Bluche, Christopher Kermorvant, Jérôme Louradour
Date: 2013-11-05 00:00:00
Tasks: Handwriting Recognition

Recurrent neural networks (RNNs) with Long Short-Term memory cells currently
hold the best known results in unconstrained handwriting recognition. We show
that their performance can be greatly improved using dropout - a recently
proposed regularization method for deep architectures. While previous works
showed that dropout gave superior performance in the context of convolutional
networks, it had never been applied to RNNs. In our approach, dropout is
carefully used in the network so that it does not affect the recurrent
connections, hence the power of RNNs in modeling sequence is preserved.
Extensive experiments on a broad range of handwritten databases confirm the
effectiveness of dropout on deep architectures even when the network mainly
consists of recurrent and shared connections.
Collaborative Representation based Classification for Face Recognition
Authors: Lei Zhang, Meng Yang, Xiangchu Feng, Yi Ma, David Zhang
Date: 2012-04-11 00:00:00
Tasks: Classification, Face Recognition, General Classification, Robust Face Recognition, Sparse Representation-based Classification

By coding a query sample as a sparse linear combination of all training samples and then classifying it by evaluating which class leads to the minimal coding residual, sparse representation based classification (SRC) leads to interesting results for robust face recognition. It is widely believed that the l1- norm sparsity constraint on coding coefficients plays a key role in the success of SRC, while its use of all training samples to collaboratively represent the query sample is rather ignored. In this paper we discuss how SRC works, and show that the collaborative representation mechanism used in SRC is much more crucial to its success of face classification. The SRC is a special case of collaborative representation based classification (CRC), which has various instantiations by applying different norms to the coding residual and coding coefficient. More specifically, the l1 or l2 norm characterization of coding residual is related to the robustness of CRC to outlier facial pixels, while the l1 or l2 norm characterization of coding coefficient is related to the degree of discrimination of facial features. Extensive experiments were conducted to verify the face recognition accuracy and efficiency of CRC with different instantiations.
Parsing using a grammar of word association vectors
Authors: Robert John Freeman
Date: 2014-03-10 00:00:00

This paper was was first drafted in 2001 as a formalization of the system
described in U.S. patent U.S. 7,392,174. It describes a system for implementing
a parser based on a kind of cross-product over vectors of contextually similar
words. It is being published now in response to nascent interest in vector
combination models of syntax and semantics. The method used aggressive
substitution of contextually similar words and word groups to enable product
vectors to stay in the same space as their operands and make entire sentences
comparable syntactically, and potentially semantically. The vectors generated
had sufficient representational strength to generate parse trees at least
comparable with contemporary symbolic parsers.
Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets
Authors: Sofia Triantafillou, Ioannis Tsamardinos
Date: 2014-03-10 00:00:00
Tasks: Causal Discovery

Scientific practice typically involves repeatedly studying a system, each
time trying to unravel a different perspective. In each study, the scientist
may take measurements under different experimental conditions (interventions,
manipulations, perturbations) and measure different sets of quantities
(variables). The result is a collection of heterogeneous data sets coming from
different data distributions. In this work, we present algorithm COmbINE, which
accepts a collection of data sets over overlapping variable sets under
different experimental conditions; COmbINE then outputs a summary of all causal
models indicating the invariant and variant structural characteristics of all
models that simultaneously fit all of the input data sets. COmbINE converts
estimated dependencies and independencies in the data into path constraints on
the data-generating causal model and encodes them as a SAT instance. The
algorithm is sound and complete in the sample limit. To account for conflicting
constraints arising from statistical errors, we introduce a general method for
sorting constraints in order of confidence, computed as a function of their
corresponding p-values. In our empirical evaluation, COmbINE outperforms in
terms of efficiency the only pre-existing similar algorithm; the latter
additionally admits feedback cycles, but does not admit conflicting constraints
which hinders the applicability on real data. As a proof-of-concept, COmbINE is
employed to co-analyze 4 real, mass-cytometry data sets measuring
phosphorylated protein concentrations of overlapping protein sets under 3
different interventions.
Generating Music from Literature
Authors: Hannah Davis, Saif M. Mohammad
Date: 2014-03-10 00:00:00
Proceeding: WS 2014 4

We present a system, TransProse, that automatically generates musical pieces
from text. TransProse uses known relations between elements of music such as
tempo and scale, and the emotions they evoke. Further, it uses a novel
mechanism to determine sequences of notes that capture the emotional activity
in the text. The work has applications in information visualization, in
creating audio-visual e-books, and in developing music apps.
Learning Factored Representations in a Deep Mixture of Experts
Authors: David Eigen, Marc'Aurelio Ranzato, Ilya Sutskever
Date: 2013-12-16 00:00:00
Tasks: Mixture-of-Experts

Mixtures of Experts combine the outputs of several "expert" networks, each of
which specializes in a different part of the input space. This is achieved by
training a "gating" network that maps each input to a distribution over the
experts. Such models show promise for building larger networks that are still
cheap to compute at test time, and more parallelizable at training time. In
this this work, we extend the Mixture of Experts to a stacked model, the Deep
Mixture of Experts, with multiple sets of gating and experts. This
exponentially increases the number of effective experts by associating each
input with a combination of experts at each layer, yet maintains a modest model
size. On a randomly translated version of the MNIST dataset, we find that the
Deep Mixture of Experts automatically learns to develop location-dependent
("where") experts at the first layer, and class-specific ("what") experts at
the second layer. In addition, we see that the different combinations are in
use when the model is applied to a dataset of speech monophones. These
demonstrate effective use of all expert combinations.
From average case complexity to improper learning complexity
Authors: Amit Daniely, Nati Linial, Shai Shalev-Shwartz
Date: 2013-11-10 00:00:00
Tasks: Learning Theory

The basic problem in the PAC model of computational learning theory is to
determine which hypothesis classes are efficiently learnable. There is
presently a dearth of results showing hardness of learning problems. Moreover,
the existing lower bounds fall short of the best known algorithms.
  The biggest challenge in proving complexity results is to establish hardness
of {\em improper learning} (a.k.a. representation independent learning).The
difficulty in proving lower bounds for improper learning is that the standard
reductions from $\mathbf{NP}$-hard problems do not seem to apply in this
context. There is essentially only one known approach to proving lower bounds
on improper learning. It was initiated in (Kearns and Valiant 89) and relies on
cryptographic assumptions.
  We introduce a new technique for proving hardness of improper learning, based
on reductions from problems that are hard on average. We put forward a (fairly
strong) generalization of Feige's assumption (Feige 02) about the complexity of
refuting random constraint satisfaction problems. Combining this assumption
with our new technique yields far reaching implications. In particular,
  1. Learning $\mathrm{DNF}$'s is hard.
  2. Agnostically learning halfspaces with a constant approximation ratio is
hard.
  3. Learning an intersection of $\omega(1)$ halfspaces is hard.
Learning Transformations for Clustering and Classification
Authors: Qiang Qiu, Guillermo Sapiro
Date: 2013-09-09 00:00:00
Tasks: Classification, Clustering, General Classification

A low-rank transformation learning framework for subspace clustering and
classification is here proposed. Many high-dimensional data, such as face
images and motion sequences, approximately lie in a union of low-dimensional
subspaces. The corresponding subspace clustering problem has been extensively
studied in the literature to partition such high-dimensional data into clusters
corresponding to their underlying low-dimensional subspaces. However,
low-dimensional intrinsic structures are often violated for real-world
observations, as they can be corrupted by errors or deviate from ideal models.
We propose to address this by learning a linear transformation on subspaces
using matrix rank, via its convex surrogate nuclear norm, as the optimization
criteria. The learned linear transformation restores a low-rank structure for
data from the same subspace, and, at the same time, forces a a maximally
separated structure for data from different subspaces. In this way, we reduce
variations within subspaces, and increase separation between subspaces for a
more robust subspace clustering. This proposed learned robust subspace
clustering framework significantly enhances the performance of existing
subspace clustering methods. Basic theoretical results here presented help to
further support the underlying framework. To exploit the low-rank structures of
the transformed subspaces, we further introduce a fast subspace clustering
technique, which efficiently combines robust PCA with sparse modeling. When
class labels are present at the training stage, we show this low-rank
transformation framework also significantly enhances classification
performance. Extensive experiments using public datasets are presented, showing
that the proposed approach significantly outperforms state-of-the-art methods
for subspace clustering and classification.
Generalized Canonical Correlation Analysis and Its Application to Blind Source Separation Based on a Dual-Linear Predictor Structure
Authors: Wei Liu
Date: 2014-03-09 00:00:00
Tasks: blind source separation

Blind source separation (BSS) is one of the most important and established
research topics in signal processing and many algorithms have been proposed
based on different statistical properties of the source signals. For
second-order statistics (SOS) based methods, canonical correlation analysis
(CCA) has been proved to be an effective solution to the problem. In this work,
the CCA approach is generalized to accommodate the case with added white noise
and it is then applied to the BSS problem for noisy mixtures. In this approach,
the noise component is assumed to be spatially and temporally white, but the
variance information of noise is not required. An adaptive blind source
extraction algorithm is derived based on this idea and a further extension is
proposed by employing a dual-linear predictor structure for blind source
extraction (BSE).
Texture Defect Detection in Gradient Space
Authors: V. Asha, N. U. Bhajantri, P. Nagabhushan
Date: 2014-03-09 00:00:00
Tasks: Defect Detection

In this paper, we propose a machine vision algorithm for automatically
detecting defects in patterned textures with the help of gradient space and its
energy. Experiments on real fabric images with defects show that the proposed
method can be used for automatic detection of fabric defects in textile
industries.
Fast Distribution To Real Regression
Authors: Junier B. Oliva, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing
Date: 2013-11-10 00:00:00
Tasks: regression

We study the problem of distribution to real-value regression, where one aims
to regress a mapping $f$ that takes in a distribution input covariate $P\in
\mathcal{I}$ (for a non-parametric family of distributions $\mathcal{I}$) and
outputs a real-valued response $Y=f(P) + \epsilon$. This setting was recently
studied, and a "Kernel-Kernel" estimator was introduced and shown to have a
polynomial rate of convergence. However, evaluating a new prediction with the
Kernel-Kernel estimator scales as $\Omega(N)$. This causes the difficult
situation where a large amount of data may be necessary for a low estimation
risk, but the computation cost of estimation becomes infeasible when the
data-set is too large. To this end, we propose the Double-Basis estimator,
which looks to alleviate this big data problem in two ways: first, the
Double-Basis estimator is shown to have a computation complexity that is
independent of the number of of instances $N$ when evaluating new predictions
after training; secondly, the Double-Basis estimator is shown to have a fast
rate of convergence for a general class of mappings $f\in\mathcal{F}$.
FuSSO: Functional Shrinkage and Selection Operator
Authors: Junier B. Oliva, Barnabas Poczos, Timothy Verstynen, Aarti Singh, Jeff Schneider, Fang-Cheng Yeh, Wen-Yih Tseng
Date: 2013-11-10 00:00:00

We present the FuSSO, a functional analogue to the LASSO, that efficiently
finds a sparse set of functional input covariates to regress a real-valued
response against. The FuSSO does so in a semi-parametric fashion, making no
parametric assumptions about the nature of input functional covariates and
assuming a linear form to the mapping of functional covariates to the response.
We provide a statistical backing for use of the FuSSO via proof of asymptotic
sparsistency under various conditions. Furthermore, we observe good results on
both synthetic and real-world data.
Natural Language Feature Selection via Cooccurrence
Authors: Michael Stewart
Date: 2014-03-08 00:00:00
Tasks: feature selection, Specificity

Specificity is important for extracting collocations, keyphrases, multi-word
and index terms [Newman et al. 2012]. It is also useful for tagging, ontology
construction [Ryu and Choi 2006], and automatic summarization of documents
[Louis and Nenkova 2011, Chali and Hassan 2012]. Term frequency and
inverse-document frequency (TF-IDF) are typically used to do this, but fail to
take advantage of the semantic relationships between terms [Church and Gale
1995]. The result is that general idiomatic terms are mistaken for specific
terms. We demonstrate use of relational data for estimation of term
specificity. The specificity of a term can be learned from its distribution of
relations with other terms. This technique is useful for identifying relevant
words or terms for other natural language processing tasks.
Compressive Nonparametric Graphical Model Selection For Time Series
Authors: Alexander Jung, Reinhard Heckel, Helmut Bölcskei, Franz Hlawatsch
Date: 2013-11-13 00:00:00
Tasks: Model Selection, Time Series, Time Series Analysis

We propose a method for inferring the conditional indepen- dence graph (CIG)
of a high-dimensional discrete-time Gaus- sian vector random process from
finite-length observations. Our approach does not rely on a parametric model
(such as, e.g., an autoregressive model) for the vector random process; rather,
it only assumes certain spectral smoothness proper- ties. The proposed
inference scheme is compressive in that it works for sample sizes that are
(much) smaller than the number of scalar process components. We provide
analytical conditions for our method to correctly identify the CIG with high
probability.
Designing an FPGA Synthesizable Computer Vision Algorithm to Detect the Greening of Potatoes
Authors: Jaspinder Pal Singh
Date: 2014-03-08 00:00:00
Tasks: General Classification

Potato quality control has improved in the last years thanks to automation
techniques like machine vision, mainly making the classification task between
different quality degrees faster, safer and less subjective. In our study we
are going to design a computer vision algorithm for grading of potatoes
according to the greening of the surface color of potato. The ratio of green
pixels to the total number of pixels of the potato surface is found. The higher
the ratio the worse is the potato. First the image is converted into serial
data and then processing is done in RGB colour space. Green part of the potato
is also shown by de-serializing the output. The same algorithm is then
synthesized on FPGA and the result shows thousand times speed improvement in
case of hardware synthesis.
Logics of formal inconsistency arising from systems of fuzzy logic
Authors: Marcelo Coniglio, Francesc Esteva, Lluís Godo
Date: 2013-07-13 00:00:00

This paper proposes the meeting of fuzzy logic with paraconsistency in a very
precise and foundational way. Specifically, in this paper we introduce
expansions of the fuzzy logic MTL by means of primitive operators for
consistency and inconsistency in the style of the so-called Logics of Formal
Inconsistency (LFIs). The main novelty of the present approach is the
definition of postulates for this type of operators over MTL-algebras, leading
to the definition and axiomatization of a family of logics, expansions of MTL,
whose degree-preserving counterpart are paraconsistent and moreover LFIs.
Combination of PCA with SMOTE Resampling to Boost the Prediction Rate in Lung Cancer Dataset
Authors: Mehdi Naseriparsa, Mohammad Mansour Riahi Kashani
Date: 2014-03-08 00:00:00
Tasks: Dimensionality Reduction, General Classification

Classification algorithms are unable to make reliable models on the datasets
with huge sizes. These datasets contain many irrelevant and redundant features
that mislead the classifiers. Furthermore, many huge datasets have imbalanced
class distribution which leads to bias over majority class in the
classification process. In this paper combination of unsupervised
dimensionality reduction methods with resampling is proposed and the results
are tested on Lung-Cancer dataset. In the first step PCA is applied on
Lung-Cancer dataset to compact the dataset and eliminate irrelevant features
and in the second step SMOTE resampling is carried out to balance the class
distribution and increase the variety of sample domain. Finally, Naive Bayes
classifier is applied on the resulting dataset and the results are compared and
evaluation metrics are calculated. The experiments show the effectiveness of
the proposed method across four evaluation metrics: Overall accuracy, False
Positive Rate, Precision, Recall.