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2013 | Buch

On the Expressivity of Alignment-Based Distance and Similarity Measures on Sequences and Trees in Inducing Orderings Kapitel lesen Erstes Kapitel lesen

Mathematical Methodologies in Pattern Recognition and Machine Learning

Contributions from the International Conference on Pattern Recognition Applications and Methods, 2012

herausgegeben von: Pedro Latorre Carmona, J. Salvador Sánchez, Ana L.N. Fred

Verlag: Springer New York

Buchreihe : Springer Proceedings in Mathematics & Statistics

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This volume features key contributions from the International Conference on Pattern Recognition Applications and Methods, (ICPRAM 2012,) held in Vilamoura, Algarve, Portugal from February 6th-8th, 2012. The conference provided a major point of collaboration between researchers, engineers and practitioners in the areas of Pattern Recognition, both from theoretical and applied perspectives, with a focus on mathematical methodologies. Contributions describe applications of pattern recognition techniques to real-world problems, interdisciplinary research, and experimental and theoretical studies which yield new insights that provide key advances in the field.

This book will be suitable for scientists and researchers in optimization, numerical methods, computer science, statistics and for differential geometers and mathematical physicists.

Inhaltsverzeichnis

Frontmatter
On the Expressivity of Alignment-Based Distance and Similarity Measures on Sequences and Trees in Inducing Orderings
Abstract
Both ‘distance’ and ‘similarity’ measures have been proposed for the comparison of sequences and for the comparison of trees, based on scoring mappings. For a given alphabet of node-labels, the measures are parameterised by a table giving label-dependent values for swaps, deletions and insertions. The paper addresses the question whether an ordering by a ‘distance’ measure, with some parameter setting, can be also expressed by a ‘similarity’ measure, with some other parameter setting, and vice versa. Ordering of three kinds is considered: alignment-orderings, for fixed source S and target T, neighbour-orderings, where for a fixed S, varying candidate neighbours T i are ranked, and pair-orderings, where for varying S i , and varying T j , the pairings \(\langle {S}_{i},{T}_{j}\rangle\) are ranked. We show that (1) any alignment-ordering expressed by ‘distance’ setting be re-expressed by a ‘similarity’ setting, and vice versa; (2) any neigbour-ordering and pair-ordering expressed by a ‘distance’ setting be re-expressed by a ‘similarity’ setting; (3) there are neighbour-orderings and pair-orderings expressed by a ‘similarity’ setting which cannot be expressed by a ‘similarity’ setting. A consequence of this is that there are categorisation and hierarchical clustering outcomes which can be achieved via similarity but not via
Martin Emms, Hector-Hugo Franco-Penya
Automatic Annotation of a Dynamic Corpus by Label Propagation
Abstract
We are interested in the problem of automatically annotating a large, constantly expanding corpus, in the case where potentially neither the dataset nor the class of possible labels that can be used are static, and the annotation of the data needs to be efficient. This application is motivated by real-world scenarios of news content analysis and social-web content analysis. We investigate a method based on the creation of a graph, whose vertices are the documents and the edges represent some notion of semantic similarity. In this graph, label propagation algorithms can be efficiently used to apply labels to documents based on the annotation of their neighbours. This paper presents experimental results about both the efficient creation of the graph and the propagation of the labels. We compare the effectiveness of various approaches to graph construction by building graphs of 800,000 vertices based on the Reuters corpus, showing that relation-based classification is competitive with support vector machines, which can be considered as state of the art. We also show that the combination of our relation-based approach and support vector machines leads to an improvement over the methods individually.
Thomas Lansdall-Welfare, Ilias Flaounas, Nello Cristianini
Computing Voronoi Adjacencies in High Dimensional Spaces by Using Linear Programming
Abstract
Some algorithms in Pattern Recognition and Machine Learning as neighborhood-based classification and dataset condensation can be improved with the use of Voronoi tessellation. This paper shows the weakness of some existing algorithms of tessellation to deal with high-dimensional datasets. The use of linear programming can improve the tessellation procedures by focusing on Voronoi adjacency. It will be shown that the adjacency test based on linear programming is a version of the polytope search. However, the polytope search procedure provides more information than a simple Boolean test. This paper proposes a strategy to use the additional information contained in the basis of the linear programming algorithm to obtain other tests. The theoretical results are applied to tessellate several random datasets, and also for much-used datasets in Machine Learning repositories.
Juan Mendez, Javier Lorenzo
Phase-Locked Matrix Factorization with Estimation of the Common Oscillation
Abstract
Phase-Locked Matrix Factorization (PLMF) is an algorithm to perform separation of synchronous sources. Such a problem cannot be addressed by orthodox methods such as Independent Component Analysis, because synchronous sources are highly mutually dependent. PLMF separates available data into the mixing matrix and the sources; the sources are then decomposed into amplitude and phase components. Previously, PLMF was applicable only if the oscillatory component, common to all synchronized sources, was known, which is clearly a restrictive assumption. The main goal of this paper is to present a version of PLMF where this assumption is no longer needed—the oscillatory component can be estimated alongside all the other variables, thus making PLMF much more applicable to real-world data. Furthermore, the optimization procedures in the original PLMF are improved. Results on simulated data illustrate that this new approach successfully estimates the oscillatory component, together with the remaining variables, showing that the general problem of separation of synchronous sources can now be tackled.
Miguel Almeida, Ricardo Vigário, José Bioucas-Dias
Stochastic Subgradient Estimation Training for Support Vector Machines
Abstract
Subgradient algorithms for training support vector machines have been successful in solving many large-scale and online learning problems. However, for the most part, their applicability has been restricted to linear kernels and strongly convex formulations. This paper describes efficient subgradient approaches without such limitations. Our approaches make use of randomized low-dimensional approximations to nonlinear kernels, and minimization of a reduced primal formulation using an algorithm based on robust stochastic approximation, which does not require strong convexity. Experiments illustrate that our approaches produce solutions of comparable prediction accuracy with the solutions acquired from existing SVM solvers, but often in much shorter time.
Sangkyun Lee, Stephen J. Wright
Single-Frame Signal Recovery Using a Similarity-Prior
Abstract
We consider the problem of signal reconstruction from noisy observations in a highly under-determined problem setting. Most of previous work does not consider any specific extra information to recover the signal. Here we address this problem by exploiting the similarity between the signal of interest and a consecutive motionless frame. We incorporate this additional information of similarity that is available into a probabilistic image-prior based on the Pearson type VII Markov Random Field model. Results on both synthetic and real data of MRI images demonstrate the effectiveness of our method in both compressed setting and classical super-resolution experiments.
Sakinah A. Pitchay, Ata Kabán
A Discretized Newton Flow for Time-Varying Linear Inverse Problems
Abstract
The reconstruction of a signal from only a few measurements, deconvolving, or denoising are only a few interesting signal processing applications that can be formulated as linear inverse problems. Commonly, one overcomes the ill-posedness of such problems by finding solutions that match some prior assumptions on the signal best. These are often sparsity assumptions as in the theory of Compressive Sensing. In this paper, we propose a method to track the solutions of linear inverse problems, and consider the two conceptually different approaches based on the synthesis and the analysis signal model. We assume that the corresponding solutions vary smoothly over time. A discretized Newton flow allows to incorporate the time varying information for tracking and predicting the subsequent solution. This prediction requires to solve a linear system of equations, which is in general computationally cheaper than solving a new inverse problem. It may also serve as an additional prior that takes the smooth variation of the solutions into account, or as an initial guess for the preceding reconstruction. We exemplify our approach with the reconstruction of a compressively sampled synthetic video sequence.
Martin Kleinsteuber, Simon Hawe
Exploiting Structural Consistencies with Stacked Conditional Random Fields
Abstract
Conditional Random Fields (CRF) are popular methods for labeling unstructured or textual data. Like many machine learning approaches, these undirected graphical models assume the instances to be independently distributed. However, in real-world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. We apply rule learning collectively on the predictions of an initial CRF for one context to acquire descriptions of its specific properties. Then, we utilize these descriptions as dynamic and high quality features in an additional (stacked) CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.
Peter Kluegl, Martin Toepfer, Florian Lemmerich, Andreas Hotho, Frank Puppe
Detecting Mean-Reverted Patterns in Algorithmic Pairs Trading
Abstract
This paper proposes a methodology for detecting mean-reverted segments of data streams in algorithmic pairs trading. Considering a state-space model that describes the spread (data stream) as the difference of the prices of two assets, we propose two new recursive least squares (RLS) algorithms for predicting mean-reversion of the spread in real time. The first is a combination of steepest descent RLS and Gauss–Newton RLS, for which we extend previous work by providing exact recursive equations to update the variable forgetting factor (VFF). We propose a new RLS algorithm for variable forgetting, by transforming the prediction errors into a binary process and adopting Bayesian methods for inference. The new approach is versatile as compared to more traditional RLS schemes, having the advantage of uncertainty analysis around the VFF. The methods are illustrated with real data, consisting of daily prices of Target Corporation and Walmart Stores Inc shares, over a period of 6 years. Alongside the detection of mean-reversion of the spread, we implement a simple trading strategy. The empirical results suggest that the new Bayesian approach returns are in excess of 130% cumulative profit over a period of 2 years.
K. Triantafyllopoulos, S. Han
Segmenting Carotid in CT Using Geometric Potential Field Deformable Model
Abstract
We present a method for the reconstruction of vascular geometries from medical images. Image denoising is performed using vessel enhancing diffusion, which can smooth out image noise and enhance vessel structures. The Canny edge detection technique, which produces object edges with single pixel width, is used for accurate detection of the lumen boundaries. The image gradients are then used to compute the geometric potential field which gives a global representation of the geometric configuration. The deformable model uses a regional constraint to suppress calcified regions for accurate segmentation of the vessel geometries. The proposed framework shows high accuracy when applied to the segmentation of the carotid arteries from CT images.
Si Yong Yeo, Xianghua Xie, Igor Sazonov, Perumal Nithiarasu
A Robust Deformable Model for 3D Segmentation of the Left Ventricle from Ultrasound Data
Abstract
This paper presents a novel bottom-up deformable-based model for the segmentation of the Left Ventricle (LV) in 3D ultrasound data. The methodology presented here is based on Probabilistic Data Association Filter (PDAF). The main steps that characterize the proposed approach can be summarized as follows. After a rough initialization given by the user, the following steps are performed: (1) low-level transition edge points are detected based on a prior model for the intensity of the LV, (2) middle-level features or patch formation is accomplished by linking the low-level information, (3) data interpretations are computed (hypothesis) based on the reliability (belonging or not to the LV boundary) of the previously obtained patches, (4) a confidence degree is assigned to each data interpretation and the model is updated taking into account all data interpretations.Results testify the usefulness of the approach in both synthetic and real LV volumes data. The obtained LV segmentations are compared with expert’s manual segmentations, yielding an average distance of 3 mm between them.
Carlos Santiago, Jorge S. Marques, Jacinto C. Nascimento
Facial Expression Recognition Using Diffeomorphic Image Registration Framework
Abstract
This paper presents a new method for facial expression modelling and recognition based on diffeomorphic image registration parameterised via stationary velocity fields in the log-Euclidean framework. The validation and comparison are done using different statistical shape models (SSM) built using the Point Distribution Model (PDM), velocity fields and deformation fields. The obtained results show that the facial expression representation based on stationary velocity fields can be successfully utilised in facial expression recognition, and this parameterisation produces a higher recognition rate than the facial expression representation based on deformation fields.
Bartlomiej W. Papiez, Bogdan J. Matuszewski, Lik-Kwan Shark, Wei Quan
Metadaten
Titel
Mathematical Methodologies in Pattern Recognition and Machine Learning
herausgegeben von
Pedro Latorre Carmona
J. Salvador Sánchez
Ana L.N. Fred
Copyright-Jahr
2013
Verlag
Springer New York
Electronic ISBN
978-1-4614-5076-4
Print ISBN
978-1-4614-5075-7
DOI
https://doi.org/10.1007/978-1-4614-5076-4

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