A method for combining mutual information and canonical correlation analysis: Predictive Mutual Information and its use in feature selection

Feature selection is a critical step in many artificial intelligence and pattern recognition problems. Shannon's Mutual Information (MI) is a classical and widely used measure of dependence measure that serves as a good feature selection algorithm. However, as it is a measure of mutual information in average, under-sampled classes (rare events) can be overlooked by this measure, which can cause critical false negatives (missing a relevant feature very predictive of some rare but important classes). Shannon's mutual information requires a well sampled database, which is not typical of many fields of modern science (such as biomedical), in which there are limited number of samples to learn from, or at least, not all the classes of the target function (such as certain phenotypes in biomedical) are well-sampled. On the other hand, Kernel Canonical Correlation Analysis (KCCA) is a nonlinear correlation measure effectively used to detect independence but its use for feature selection or ranking is limited due to the fact that its formulation is not intended to measure the amount of information (entropy) of the dependence. In this paper, we propose a hybrid measure of relevance, Predictive Mutual Information (PMI) based on MI, which also accounts for predictability of signals from each other in its calculation as in KCCA. We show that PMI has more improved feature detection capability than MI, especially in catching suspicious coincidences that are rare but potentially important not only for experimental studies but also for building computational models. We demonstrate the usefulness of PM!, and superiority over MI, on both toy and real datasets. (C) 2011 Elsevier Ltd. All rights reserved

Telediagnosis of Parkinson's Disease Using Measurements of Dysphonia

Parkinson's disease (PD) is a neurological illness which impairs motor skills, speech, and other functions such as mood, behavior, thinking, and sensation. It causes vocal impairment for approximately 90% of the patients. As the symptoms of PD occur gradually and mostly targeting the elderly people for whom physical visits to the clinic are inconvenient and costly, telemonitoring of the disease using measurements of dysphonia (vocal features) has a vital role in its early diagnosis. Such dysphonia features extracted from the voice come in variety and most of them are interrelated. The purpose of this study is twofold: (1) to select a minimal subset of features with maximal joint relevance to the PD-score, a binary score indicating whether or not the sample belongs to a person with PD; and (2) to build a predictive model with minimal bias (i.e. to maximize the generalization of the predictions so as to perform well with unseen test examples). For these tasks, we apply the mutual information measure with the permutation test for assessing the relevance and the statistical significance of the relations between the features and the PD-score, rank the features according to the maximum-relevance-minimum-redundancy (mRMR) criterion, use a Support Vector Machine (SVM) for building a classification model and test it with a more suitable cross-validation scheme that we called leave-one-individual-out that fits with the dataset in hand better than the conventional bootstrapping or leave-one-out validation methods

Prosthodontic treatment of a patient with hemimandibular elongation: A clinical report

This clinical report describes prosthodontic treatment of a patient with hemimandibular elongation that resulted in significant dentofacial asymmetry. A maxillary metal-ceramic fixed dental prosthesis and crowns were fabricated. To eliminate the negative horizontal overlap, 3 mandibular teeth were reduced to the gingival level. The mandibular prosthetic restoration was completed with metal and composite resin using an electroforming technique

Rehabilitation of Occlusal Vertical Dimension in a Patient with Acromegaly: A Clinical Report

Acromegaly is a rare acquired disorder caused by excessive growth hormone production. Dentists play an important role in the diagnosis of this disorder because of intraoral and extraoral symptoms such as extreme growth of the mandible, enlargement of the maxilla, diastema between teeth, a tendency toward malocclusion, a wide and thick nose, a marked malar bone, and thick lips. The prosthetic treatment of these patients is challenging because growth in the condyles and rami can lead to the development of a severe class III jaw relationship

Analysis of shared miRNAs of different species using ensemble CCA and genetic distance

MicroRNA is a type of single stranded RNA molecule and has an important role for gene expression. Although there have been a number of computational methodologies in bioinformatics research for miRNA classification and target prediction tasks, analysis of shared miRNAs among different species has not yet been addressed. In this article, we analyzed miRNAs that have the same name and function but have different sequences and belong to different (but closely related) species which are constructed from the online miRBase database. We used sequence-driven features and performed the standard and the ensemble versions of Canonical Correlation Analysis (CCA). However, due to its sensitivity to noise and outliers, we extended it using an ensemble approach. Using linear combinations of dimer features, the proposed Ensemble CCA (ECCA) method has identified higher test-set-correlations than CCA. Moreover, our analysis reveals that the Redundancy Index of ECCA applied to a pair of species has correlation with their genetic distance. (C) 2015 Elsevier Ltd. All rights reserved

Ensemble canonical correlation analysis

Canonical Correlation Analysis (CCA) aims at identifying linear dependencies between two different but related multivariate views of the same underlying semantics. Ignoring its various extensions to more than two views, CCA uses these two views as complex labels to guide the search of maximally correlated projection vectors (covariates). Therefore, CCA can overfit the training data, meaning that different correlated projections can be found when the two-view training dataset is resampled. Although, to avoid such overfitting, ensemble approaches that utilize resampling techniques have been effectively used for improving generalization of many machine learning methods, an ensemble approach has not yet been formulated for CCA. In this paper, we propose an ensemble method for obtaining a final set of covariates by combining multiple sets of covariates extracted from subsamples. In comparison to those obtained by the application of the classical CCA on the whole set of training data, combining covariates with weaker correlations extracted from a number of subsamples of the training data produces stronger correlations that generalize to unseen test examples. Experimental results on emotion recognition, digit recognition, content-based retrieval, and multiple view object recognition have shown that ensemble CCA has better generalization for both the test set correlations of the covariates and the test set accuracy of classification performed on these covariates

Prediction of Level and Abrupt Changes of Ozon Concentration

While, in stratosphere, high level ozone concentration protects the Earth against ultraviolet radiation, in lower troposphere it has negative effects on human health and environment. The goal of this study is to determine the feature groups that are related to abrupt changes in the level of ozone. Linear discriminant analysis and support vector machines methods are used to explore which combination of features are predictive of abrupt changes in ozone level on the simulation dataset collected in Ankara, Turkey, by an automatic air quality monitoring station operated by the ministry of environment and urban planning. The dataset consists of one year of measurements of air pollutants and the meteorological factors. The obtained results showed that particulate matters, nitric oxides and temperature are most effective parameters in the classification of absurt rise and fall in the level of ozone

Combined prosthodontic and orthodontic treatment of a patient with a Class III skeletal malocclusion: A clinical report

This clinical report describes a multidisciplinary approach for the treatment of a patient with Angle Class III skeletal malocclusion and decreased occlusal vertical dimension. An overlay removable partial denture (ORPD) was used to reestablish the occlusal vertical dimension (OVD). After the trial and adjustment period, the reduced lower anterior dentofacial height was orthodontically increased and the negative horizontal overlap was corrected. A maxillary precision attachment RPD and a mandibular fixed partial denture and metal ceramic crowns were fabricated to satisfy esthetic and functional requirements

A feature selection method based on kernel canonical correlation analysis and the minimum Redundancy-Maximum Relevance filter method

In this paper, we propose a feature selection method based on a recently popular minimum Redundancy-Maximum Relevance (mRMR) criterion, which we called Kernel Canonical Correlation Analysis based mRMR (KCCAmRMR) based on the idea of finding the unique information, i.e. information that is distinct from the set of already selected variables, that a candidate variable possesses about the target variable. In simplest terms, for this purpose, we propose using correlated functions explored by KCCA instead of using the features themselves as inputs to mRMR. We demonstrate the usefulness of our method on both toy and benchmark datasets. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved