Feature Outlier Research Articles

A Visual Analytics Approach Using the Exploration of Multidimensional Feature Spaces for Content-Based Medical Image Retrieval.

Content-based image retrieval (CBIR) is a search technique based on the similarity of visual features and has demonstrated potential benefits for medical diagnosis, education, and research. However, clinical adoption of CBIR is partially hindered by the difference between the computed image similarity and the user's search intent, the semantic gap, with the end result that relevant images with outlier features may not be retrieved. Furthermore, most CBIR algorithms do not provide intuitive explanations as to why the retrieved images were considered similar to the query (e.g., which subset of features were similar), hence, it is difficult for users to verify if relevant images, with a small subset of outlier features, were missed. Users, therefore, resort to examining irrelevant images and there are limited opportunities to discover these "missed" images. In this paper, we propose a new approach to medical CBIR by enabling a guided visual exploration of the search space through a tool, called visual analytics for medical image retrieval (VAMIR). The visual analytics approach facilitates interactive exploration of the entire dataset using the query image as a point-of-reference. We conducted a user study and several case studies to demonstrate the capabilities of VAMIR in the retrieval of computed tomography images and multimodality positron emission tomography and computed tomography images.

YouRank: Let User Engagement Rank Microblog Search Results

We propose an approach for ranking microblog search results. The basic idea is to leverage user engagement for the purpose of ranking: if a microblog post received many retweets/replies, this means users find it important and it should be ranked higher. However, simply applying the raw count of engagement may bias the ranking by favoring posts from celebrity users whose posts generally receive a disproportionate amount of engagement regardless of the contents of posts. To reduce this bias, we propose a variety of time window-based outlier features that transfer the raw engagement count into an importance score, on a per user basis. The evaluation on five real-world datasets confirms that the proposed approach can be used to improve microblog search.

A Case Study of Spectral Signature Detection in Multimodal and Outlier-Contaminated Scenes

Mapping localized spectral features in complex scenes demands sensitive and robust detection algorithms. This letter investigates two aspects of large images that can harm matched filter (MF) detection performance. First, multimodal backgrounds may violate normality assumptions. Second, outlier features can trigger false detections due to large projections onto the target vector. We review two state-of-the-art methods designed to resolve these issues. The background clustering of Funk models multimodal backgrounds, and the mixture-tuned (MT) MF of Boardman and Kruse addresses outliers. We demonstrate that combining the two methods has additional performance benefits. An MT cluster MF shows effective performance on simulated and airborne data sets. We demonstrate target detection scenarios that evidence multimodality, outliers, and their combination. These experiments explore the performance of the component algorithms and the practical circumstances that can favor a combined approach.

Robust image annotation via simultaneous feature and sample outlier pursuit

Graph-based semi-supervised image annotation has achieved great success in a variety of studies, yet it essentially and intuitively suffers from both the irrelevant/noisy features (referred to as feature outliers) and the unusual/corrupted samples (referred to as sample outliers). In this work, we investigate how to derive robust sample affinity matrix via simultaneous feature and sample outlier pursuit. This task is formulated as a Dual-outlier and Prior-driven Low-Rank Representation (DP-LRR) problem, which possesses convexity in objective function. In DP-LRR, the clean data are assumed to be self-reconstructible with low-rank coefficient matrix as in LRR; while the error matrix is decomposed as the sum of a row-wise sparse matrix and a column-wise sparse matrix, the ℓ 2,1 -norm minimization of which encourages the pursuit of feature and sample outliers respectively. The DP-LRR is further regularized by the priors from side information, that is, the inhomogeneous data pairs. An efficient iterative procedure based on linearized alternating direction method is presented to solve the DP-LRR problem, with closed-form solutions within each iteration. The derived low-rank reconstruction coefficient matrix is then fed into any graph based semi-supervised label propagation algorithm for image annotation, and as a by-product, the cleaned data from DP-LRR can also be utilized as a better image representation to generally boost image annotation performance. Extensive experiments on MIRFlickr, Corel30K, NUS-WIDE-LITE and NUS-WIDE databases well demonstrate the effectiveness of the proposed formulation for robust image annotation.

A Hybrid Data Association Approach for SLAM in Dynamic Environments

Data association is critical for Simultaneous Localization and Mapping (SLAM). In a real environment, dynamic obstacles will lead to false data associations which compromise SLAM results. This paper presents a simple and effective data association method for SLAM in dynamic environments. A hybrid approach of data association based on local maps by combining ICNN and JCBB algorithms is used initially. Secondly, we set a judging condition of outlier features in association assumptions and then the static and dynamic features are detected according to spatial and temporal difference. Finally, association assumptions are updated by filtering out the dynamic features. Simulations and experimental results show that this method is feasible.

Detection of Outliers in a Time Series of Available Parking Spaces

With the provision of any source of real-time information, the timeliness and accuracy of the data provided are paramount to the effectiveness and success of the system and its acceptance by the users. In order to improve the accuracy and reliability of parking guidance systems (PGSs), the technique of outlier mining has been introduced for detecting and analysing outliers in available parking space (APS) datasets. To distinguish outlier features from the APS’s overall periodic tendency, and to simultaneously identify the two types of outliers which naturally exist in APS datasets with intrinsically distinct statistical features, a two-phase detection method is proposed whereby an improved density-based detection algorithm named “local entropy based weighted outlier detection” (EWOD) is also incorporated. Real-world data from parking facilities in the City of Newcastle upon Tyne was used to test the hypothesis. Thereafter, experimental tests were carried out for a comparative study in which the outlier detection performances of the two-phase detection method, statistic-based method, and traditional density-based method were compared and contrasted. The results showed that the proposed method can identify two different kinds of outliers simultaneously and can give a high identifying accuracy of 100% and 92.7% for the first and second types of outliers, respectively.

Fast moving object detection with non-stationary background

The detection of moving objects under a free-moving camera is a difficult problem because the camera and object motions are mixed together and the objects are often detected into the separated components. To tackle this problem, we propose a fast moving object detection method using optical flow clustering and Delaunay triangulation as follows. First, we extract the corner feature points using Harris corner detector and compute optical flow vectors at the extracted corner feature points. Second, we cluster the optical flow vectors using K-means clustering method and reject the outlier feature points using Random Sample Consensus algorithm. Third, we classify each cluster into the camera and object motion using its scatteredness of optical flow vectors. Fourth, we compensate the camera motion using the multi-resolution block-based motion propagation method and detect the objects using the background subtraction between the previous frame and the motion compensated current frame. Finally, we merge the separately detected objects using Delaunay triangulation. The experimental results using Carnegie Mellon University database show that the proposed moving object detection method outperforms the existing other methods in terms of detection accuracy and processing time.

MCOPA: analysis of heterogeneous features in cancer expression data

BackgroundCancer outlier profile analysis (COPA) has proven to be an effective approach to analyzing cancer expression data, leading to the discovery of the TMPRSS2 and ETS family gene fusion events in prostate cancer. However, the original COPA algorithm did not identify down-regulated outliers, and the currently available R package implementing the method is similarly restricted to the analysis of over-expressed outliers. Here we present a modified outlier detection method, mCOPA, which contains refinements to the outlier-detection algorithm, identifies both over- and under-expressed outliers, is freely available, and can be applied to any expression dataset.ResultsWe compare our method to other feature-selection approaches, and demonstrate that mCOPA frequently selects more-informative features than do differential expression or variance-based feature selection approaches, and is able to recover observed clinical subtypes more consistently. We demonstrate the application of mCOPA to prostate cancer expression data, and explore the use of outliers in clustering, pathway analysis, and the identification of tumour suppressors. We analyse the under-expressed outliers to identify known and novel prostate cancer tumour suppressor genes, validating these against data in Oncomine and the Cancer Gene Index. We also demonstrate how a combination of outlier analysis and pathway analysis can identify molecular mechanisms disrupted in individual tumours.ConclusionsWe demonstrate that mCOPA offers advantages, compared to differential expression or variance, in selecting outlier features, and that the features so selected are better able to assign samples to clinically annotated subtypes. Further, we show that the biology explored by outlier analysis differs from that uncovered in differential expression or variance analysis. mCOPA is an important new tool for the exploration of cancer datasets and the discovery of new cancer subtypes, and can be combined with pathway and functional analysis approaches to discover mechanisms underpinning heterogeneity in cancers.

Fuzzy quartile encoding as a preprocessing method for biomedical pattern classification

A fuzzy set based preprocessing method is described that may be used in the classification of patterns. This method, dispersion-adjusted fuzzy quartile encoding, determines the respective degrees to which a feature (attribute) belongs to a collection of fuzzy sets that overlap at the respective quartile boundaries of the feature. The fuzzy sets are adjusted to take into account the overall dispersion of values for a feature. The membership values are subsequently used in place of the original feature value. This transformation has a normalizing effect on the feature space and is robust to feature outliers. This preprocessing method, empirically evaluated using five biomedical datasets, is shown to improve the discriminatory power of the underlying classifiers.

A diagnostic method for simultaneous feature selection and outlier identification in linear regression

A diagnostic method along the lines of forward search is proposed to simultaneously study the effect of individual observations and features on the inferences made in linear regression. The method operates by appending dummy variables to the data matrix and performing backward selection on the augmented matrix. It outputs sequences of feature–outlier combinations which can be evaluated by plots similar to those of forward search and includes the capacity to incorporate prior knowledge, in order to mitigate issues such as collinearity. It also allows for alternative ways to understand the selection of the final model. The method is evaluated on five data sets and yields promising results.

Bleeding predisposition assessments in tonsillectomy/adenoidectomy patients using fuzzy interquartile encoded neural networks

A fuzzy set theoretic methodology is described that serves as a classification preprocessing strategy for supervised feed-forward neural networks. This methodology, fuzzy interquartile encoding, determines the respective degrees to which a feature belongs to a collection of fuzzy sets that overlap at the respective quartile boundaries of the feature. These membership values are subsequently used in place of the original feature. This transformation has a normalizing effect on the feature space and is more robust to feature outliers. Its effectiveness is scrutinized using several synthetic data sets with various underlying distributions. Fuzzy interquartile encoding is shown to consistently improve the discriminatory power of the underlying classifiers. The methodology is also applied to two biomedical data sets relating to tonsillectomy and/or adenoidectomy patients who may or may not have had a predisposition to excessive bleeding during their operation. The features of the first data set are blood sample test results acquired from a coagulation laboratory and the class labels are one of three hemostatic defects as identified by the reference tests. The second data set consists of patient responses to queries from a bleeding tendency questionnaire. Normal and abnormal class labels were derived from a hematology expert system designed in consultation with a pediatric hematologist. Fuzzy interquartile encoding effected an 11% improvement in the classification accuracy of the underlying neural network classifier with the former data set and 18% with the latter.

Georgian and kurd mtDNA sequence analysis shows a lack of correlation between languages and female genetic lineages.

Mitochondrial DNA sequences from Georgians and Kurds were analyzed in order to test the possible correlation between female lineages and languages in these two neighboring West Eurasian groups. Mitochondrial sequence pools in both populations are very similar despite their different linguistic and prehistoric backgrounds. Both populations present mtDNA lineages that clearly belong to the European gene pool, as shown by 1) similar nucleotide and sequence diversities; 2) a large number of sequences shared with the rest of European samples; 3) nonsignificant genetic distances; and 4) classification of the present lineages into the major European mtDNA haplogroups already described. The outlier position of the populations from the Caucasus according to classical genetic markers is not recognized in the present Georgian mtDNA sequence pool. This result suggests that the differentiation of mtDNA sequences in West Eurasia and the outlier features of Caucasian populations should be attributed to different processes. Moreover, the putative linguistic relationship between Caucasian groups and the Basques, another outlier population within Europe for classical genetic markers, is not detected by the analysis of mtDNA sequences.