Representative Feature Set Research Articles

A Multi-Label Learning Method Using Affinity Propagation and Support Vector Machine

Multi-label learning plays a critical role in the areas of data mining, multimedia, and machine learning. Although many multi-label approaches have been proposed, few of them have considered to de-emphasize the effect of noisy features in the learning process. To address this issue, this paper designs a new method named representative multi-label learning algorithm. Instead of considering all features, the proposed algorithm focuses only on the representative ones, via incorporating an affinity propagation algorithm, kernel formulation, and a multi-label support vector machine into the learning framework. Specifically, it first adopts an affinity propagation algorithm to select a set of representative features and capture the relationships among features. Then, the algorithm constructs the representative kernel functions to measure the similarity between data instances. Finally, a multi-label support vector machine is applied to solve the learning problem. Based on the representative multi-label learning algorithm, we further design a representative multi-label learning ensemble framework to improve the accuracy, stableness, and robustness. Experimental results show that the proposed algorithm works well on most of the datasets and outperforms the compared multi-label learning approaches.

A non-intrusive approach to classify electrical appliances based on higher-order statistics and genetic algorithm: a smart grid perspective

Electrical appliance monitoring systems have received a lot of attention in recent years. These systems can provide users with valuable information for energy saving. In this article, a non-intrusive approach to classify electrical appliances based on higher-order statistics (HOS) is proposed. Aiming at reducing the computational cost of the proposed method, Fisher's Discriminant Ration and Genetic Algorithms (GA) were used for selecting a finite set of representative features among those obtained by HOS. The core idea of using GA was to simultaneously reduce the data dimension and optimize the classifier performance. The method was carried out over experimental signals, collected from the main power service entry of a house. Eleven electrical appliances were studied and fifty current signals of each of these loads were acquired; only the transient state of these signals was analyzed. The final classification was performed by multilayer perceptron (MLP) and decision tree (DT) classifiers, reaching an overall validation efficiency of 100% and 99.5%, respectively. The proposed classifiers used only 6 extracted features (second and fourth-order cumulants) and are suitable for real-time application.

Melodic Contour and Mid-Level Global Features Applied to the Analysis of Flamenco Cantes

This work focuses on the topic of melodic characterization and similarity in a specific musical repertoire: a cappella flamenco singing, more specifically in debla and martinete styles. We propose the combination of manual and automatic description. First, we use a state-of-the-art automatic transcription method to account for general melodic similarity from music recordings. Second, we define a specific set of representative mid-level melodic features, which are manually labelled by flamenco experts. Both approaches are then contrasted and combined into a global similarity measure. This similarity measure is assessed by inspecting the clusters obtained through phylogenetic algorithms and by relating similarity to categorization in terms of style. Finally, we discuss the advantage of combining automatic and expert annotations as well as the need to include repertoire-specific descriptions for meaningful melodic characterization in traditional music collections.

Content-Based Video Recommendation System Based on Stylistic Visual Features

This paper investigates the use of automatically extracted visual features of videos in the context of recommender systems and brings some novel contributions in the domain of video recommendations. We propose a new content-based recommender system that encompasses a technique to automatically analyze video contents and to extract a set of representative stylistic features (lighting, color, and motion) grounded on existing approaches of Applied Media Theory. The evaluation of the proposed recommendations, assessed w.r.t. relevance metrics (e.g., recall) and compared with existing content-based recommender systems that exploit explicit features such as movie genre, shows that our technique leads to more accurate recommendations. Our proposed technique achieves better results not only when visual features are extracted from full-length videos, but also when the feature extraction technique operates on movie trailers, pinpointing that our approach is effective also when full-length videos are not available or when there are performance requirements. Our recommender can be used in combination with more traditional content-based recommendation techniques that exploit explicit content features associated to video files, to improve the accuracy of recommendations. Our recommender can also be used alone, to address the problem originated from video files that have no meta-data, a typical situation of popular movie-sharing websites (e.g., YouTube) where every day hundred millions of hours of videos are uploaded by users and may contain no associated information. As they lack explicit content, these items cannot be considered for recommendation purposes by conventional content-based techniques even when they could be relevant for the user.

An improved global feature selection scheme for text classification

Feature selection is known as a good solution to the high dimensionality of the feature space and mostly preferred feature selection methods for text classification are filter-based ones. In a common filter-based feature selection scheme, unique scores are assigned to features depending on their discriminative power and these features are sorted in descending order according to the scores. Then, the last step is to add top-N features to the feature set where N is generally an empirically determined number. In this paper, an improved global feature selection scheme (IGFSS) where the last step in a common feature selection scheme is modified in order to obtain a more representative feature set is proposed. Although feature set constructed by a common feature selection scheme successfully represents some of the classes, a number of classes may not be even represented. Consequently, IGFSS aims to improve the classification performance of global feature selection methods by creating a feature set representing all classes almost equally. For this purpose, a local feature selection method is used in IGFSS to label features according to their discriminative power on classes and these labels are used while producing the feature sets. Experimental results on well-known benchmark datasets with various classifiers indicate that IGFSS improves the performance of classification in terms of two widely-known metrics namely Micro-F1 and Macro-F1.

Adaptive Fuzzy Consensus Clustering Framework for Clustering Analysis of Cancer Data

Performing clustering analysis is one of the important research topics in cancer discovery using gene expression profiles, which is crucial in facilitating the successful diagnosis and treatment of cancer. While there are quite a number of research works which perform tumor clustering, few of them considers how to incorporate fuzzy theory together with an optimization process into a consensus clustering framework to improve the performance of clustering analysis. In this paper, we first propose a random double clustering based cluster ensemble framework (RDCCE) to perform tumor clustering based on gene expression data. Specifically, RDCCE generates a set of representative features using a randomly selected clustering algorithm in the ensemble, and then assigns samples to their corresponding clusters based on the grouping results. In addition, we also introduce the random double clustering based fuzzy cluster ensemble framework (RDCFCE), which is designed to improve the performance of RDCCE by integrating the newly proposed fuzzy extension model into the ensemble framework. RDCFCE adopts the normalized cut algorithm as the consensus function to summarize the fuzzy matrices generated by the fuzzy extension models, partition the consensus matrix, and obtain the final result. Finally, adaptive RDCFCE (A-RDCFCE) is proposed to optimize RDCFCE and improve the performance of RDCFCE further by adopting a self-evolutionary process (SEPP) for the parameter set. Experiments on real cancer gene expression profiles indicate that RDCFCE and A-RDCFCE works well on these data sets, and outperform most of the state-of-the-art tumor clustering algorithms.

Simultaneous Feature Selection and Extraction Using Feature Significance

Dimensionality reduction of a data set by selecting or extracting relevant and nonredundant features is an essential preprocessing step used for pattern recognition, data mining, machine learning, and multimedia indexing. Among the large amount of features present in real life data sets, only a small fraction of them is effective to represent the data set accurately. Prior to analysis of the data set, preprocessing the data to obtain a smaller set of representative features and retaining the optimal salient characteristics of the data not only decrease the processing time but also lead to more compactness of the models learned and better generalization. In this regard, a novel dimensionality reduction method is presented here that simultaneously selects and extracts features using the concept of feature significance. The method is based on maximizing both relevance and significance of the reduced feature set, whereby redundancy therein is removed. The method is generic in nature in the sense that both supervised and unsupervised feature evaluation indices can be used for simultaneously feature selection and extraction. The effectiveness of the proposed method, along with a comparison with existing feature selection and extraction methods, is demonstrated on a set of real life data sets.

Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach.

Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 ± 0.068 for initialization and 0.974 ± 0.017 for subsequent segmentation, based on an independent reference standard. The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based segmentation.

MDP Feature Extraction Technique for Offline Handwritten Gurmukhi Character Recognition

Character recognition is intricate work because of the various writing styles of different individuals. Most of the published work on handwritten character recognition problems deals with statistical features, and a few works deal with structural features, in general, and Gurmukhi script, in particular. In the present work, we propose a methodology for offline handwritten Gurmukhi character recognition by using a modified division points (MDP) feature extraction technique. We also compare this technique with other recently used feature extraction techniques, namely zoning features, diagonal features, directional features, intersection and open end points features, and transition features. To select a representative set of features is the most significant task for a character recognition system. After feature extraction, the classification stage makes use of the features extracted in the previous stage to recognize the character. In this work, we used linear-support vector machines (linear-SVM), k-nearest neighbor (k-NN), and multilayer perceptron (MLP) classifiers for recognition. For experimental analysis, we used 10,500 samples of the isolated, offline, handwritten, basic 35 akhars of Gurmukhi script. The proposed system achieved a maximum recognition accuracy of 84.57%, 85.85% and 89.20% with linear-SVM, MLP and k-NN classifiers, respectively, with a five-fold cross validation technique.

Metabolic Phenotype of the Healthy Rodent Model Using In-Vial Extraction of Dried Serum, Urine, and Cerebrospinal Fluid Spots

High-throughput multiplatform metabolomics experiments are becoming an integral part of clinical and systems biology research. Such methods call for the adoption of robust sample storage and transport formats for small volumes of biofluids. One such format is the dried biofluid spot, which combines small volume requirements with easy portability. Here, we describe ultra high-performance liquid chromatography-mass spectrometry (UHPLC-MS) metabolomics of dried rodent serum, urine, and cerebrospinal fluid spots. An in-vial extraction and UHPLC-MS analysis method was first developed and validated by fingerprinting two test fluids, rat serum and RPMI cell nutrient medium. Data for these extracts were compared in terms of (i) peak area measurements of selected features to assess reproducibility and (ii) total fingerprint variation after data pretreatment. Results showed that percentage peak area variation was found to range between 1.4 and 9.4% relative standard deviation (RSD) for a representative set of molecular features. Upon application of the method to spots bearing serum, urine or cerebrospinal fluid (CSF) from healthy rats and mice, a total of 1,182 and 2,309 reproducible molecular features were obtained in positive and negative ionization modes, respectively, of which 610 (positive) and 991 (negative) were found in both rats and mice. Feature matching was used to detect similarities and differences between biofluids, with the biggest overlap found between fingerprints obtained in urine and CSF. Our results thus demonstrate the potential of such direct fingerprinting of dried biofluid spots as a viable alternative to the use of small (10-15 μL) volumes of neat biofluids in animal studies.

Semantic memory: A feature-based analysis and new norms for Italian

Semantic norms for properties produced by native speakers are valuable tools for researchers interested in the structure of semantic memory and in category-specific semantic deficits in individuals following brain damage. The aims of this study were threefold. First, we sought to extend existing semantic norms by adopting an empirical approach to category (Exp. 1) and concept (Exp. 2) selection, in order to obtain a more representative set of semantic memory features. Second, we extensively outlined a new set of semantic production norms collected from Italian native speakers for 120 artifactual and natural basic-level concepts, using numerous measures and statistics following a feature-listing task (Exp. 3b). Finally, we aimed to create a new publicly accessible database, since only a few existing databases are publicly available online.

FACE RECOGNITION USING CURVELET-BASED TWO-DIMENSIONAL PRINCIPLE COMPONENT ANALYSIS

The task of face recognition has been actively researched in recent years because of its many applications in various domains. This paper presents a robust face recognition system using curvelet-based two-dimensional principle component analysis (2D PCA) to address the problem of human face recognition from still images. 2D PCA has advantages over PCA in evaluating the covariance matrix accurately and time complexity. Inspired by the attractive attributes of curvelets in catching the edge singularities with very few coefficients in a non-adaptive manner, we introduce the scheme of decomposing images into curvelet subbands and applying 2D PCA to create a representative feature set. Experiments were designed with different implementations of each module using standard testing database. We experimented with changing the illumination normalization procedure; comparing the baseline PCA-based method with the proposed scheme; studying effects on algorithm performance of k-nearest neighbor (kNN) classifier and Support Vector Machine (SVM) classifier in the classification process; also we experimented with different databases such as FERET, etc. High accuracy rate were achieved by the proposed scheme through a comparative study.

An Adult Image Identification System Based on Robust Skin Segmentation

In this article, the authors introduce a new algorithm to identify adult images that can effectively filter out images of naked human bodies in the internet. The algorithm detects eyes, which are known as the most salient component of a human face, and makes a statistical skin color distribution model directly from each input image by choosing reliable skin samples in facial areas near the detected eyes. Skin areas over the entire image are segmented robustly with the online constructed skin color model. The authors then extract a set of representative features characterizing naked bodies from the segmented skin areas and verify if the skin regions contain naked bodies through multilayer perceptron neural networked-based learning and inference of the representative features. Experimental results are given to demonstrate that the proposed adult image detection method can identify various types of nude images effectively compared to other conventional methods.

Recognition of Isolated Handwritten Characters in Gurmukhi Script

Isolated handwritten character recognition has been the subject of intensive research during last decades because it is useful in wide range of real world problems. It also provides a solution for processing large volumes of data automatically. Work has been done in recognizing handwritten characters in many languages like Chinese, Arabic, Devnagari, Urdu and English. The work presented in this thesis, focuses on the problem of recognition of isolated handwritten characters in Gurmukhi script. The whole process consists of two stages. The first, feature extraction stage analyzes the set of isolated characters and selects a set of features that can be used to uniquely identify characters. The performance of recognition system depends heavily on what features are being used. The selection of stable and representative set of features is the heart of recognition system. The feature extraction method Zoning, is used for extracting features of the character under consideration in this problem. In Zoning method, the frame containing the character is divided into several overlapping or non-overlapping zones and the densities of object pixels in each zone are calculated. Densities are used to form a representation. The final, classification stage is the main decision making stage of the recognition system. It uses features extracted in the feature extraction stage to identify the character. K-Nearest Neighbor and Support Vector Machine are the two classifiers used for identifying the character in the problem. In k-nearest classification method, the Euclidean distance between the test point and all the reference points is calculated in order to find K nearest neighbors, and then the obtained distances are ranked in ascending order and the reference points corresponding to the k smallest Euclidean distances are taken. The Support Vector Machine (SVM) is learning machine with very good generalization ability. SVM implements the Structural Risk Minimization Principal which seeks to minimize an upper bound of the generalization error. An SVM classifier discriminates two classes of feature vectors by generating hyper-surfaces in the feature space, which are optimal in a specific sense that is the hyper-surface obtained by the SVM optimization is guaranteed to have the maximum distance to the nearest” support vectors. SVM operate on kernel evaluations of the feature vectors. An annotated sample image database of isolated handwritten characters in Gurmukhi script has been prepared which has been used for training and testing of the system.

Progress and challenges in predicting protein-protein interaction sites

The identification of protein-protein interaction sites is an essential intermediate step for mutant design and the prediction of protein networks. In recent years a significant number of methods have been developed to predict these interface residues and here we review the current status of the field. Progress in this area requires a clear view of the methodology applied, the data sets used for training and testing the systems, and the evaluation procedures. We have analysed the impact of a representative set of features and algorithms and highlighted the problems inherent in generating reliable protein data sets and in the posterior analysis of the results. Although it is clear that there have been some improvements in methods for predicting interacting sites, several major bottlenecks remain. Proteins in complexes are still under-represented in the structural databases and in particular many proteins involved in transient complexes are still to be crystallized. We provide suggestions for effective feature selection, and make it clear that community standards for testing, training and performance measures are necessary for progress in the field.

Multifeature Object Trajectory Clustering for Video Analysis

We present a novel multifeature video object trajectory clustering algorithm that estimates common patterns of behaviors and isolates outliers. The proposed algorithm is based on four main steps, namely the extraction of a set of representative trajectory features, non-parametric clustering, cluster merging and information fusion for the identification of normal and rare object motion patterns. First we transform the trajectories into a set of feature spaces on which mean-shift identifies the modes and the corresponding clusters. Furthermore, a merging procedure is devised to refine these results by combining similar adjacent clusters. The final common patterns are estimated by fusing the clustering results across all feature spaces. Clusters corresponding to reoccurring trajectories are considered as normal, whereas sparse trajectories are associated to abnormal and rare events. The performance of the proposed algorithm is evaluated on standard data-sets and compared with state-of-the-art techniques. Experimental results show that the proposed approach outperforms state-of-the-art algorithms both in terms of accuracy and robustness in discovering common patterns in video as well as in recognizing outliers.

Static features in real-time recognition of isolated vowels at high pitch

This paper addresses the problem of automatic identification of vowels uttered in isolation by female and child speakers. In this case, the magnitude spectrum of voiced vowels is sparsely sampled since only frequencies at integer multiples of F0 are significant. This impacts negatively on the performance of vowel identification techniques that either ignore pitch or rely on global shape models. A new pitch-dependent approach to vowel identification is proposed that emerges from the concept of timbre and that defines perceptual spectral clusters (PSC) of harmonic partials. A representative set of static PSC-related features are estimated and their performance is evaluated in automatic classification tests using the Mahalanobis distance. Linear prediction features and Mel-frequency cepstral coefficients (MFCC) coefficients are used as a reference and a database of five (Portuguese) natural vowel sounds uttered by 44 speakers (including 27 child speakers) is used for training and testing the Gaussian models. Results indicate that perceptual spectral cluster (PSC) features perform better than plain linear prediction features, but perform slightly worse than MFCC features. However, PSC features have the potential to take full advantage of the pitch structure of voiced vowels, namely in the analysis of concurrent voices, or by using pitch as a normalization parameter.

Feature set decomposition for decision trees

This paper presents practical aspects of feature set decomposition in classification problems using decision trees. Feature set decomposition generalizes the task of feature selection which is extensively used in data mining. Feature selection aims to provide a representative set of features from which a classifier is constructed. On the other hand, feature set decomposition decomposes the original set of features into several subsets, and builds a classifier for each subset. The classifiers are then combined for classifying new instances. In order to examine the idea, a general framework that searches for helpful decomposition structures is proposed. This framework nests many algorithms, two of which are tested empirically over a set of benchmark datasets. The first algorithm performs a serial search while using a new Vapnik-Chervonenkis dimension bound for multiple oblivious trees as an evaluating schema. The second algorithm performs a multi-search while using wrapper evaluating schema. This work indicates that feature set decomposition can increase the accuracy of decision trees.