Distance metric learning : a two-phase approach

Distance metric learning has been successfully incorporated in many machine learning applications. The main challenge arises from the positive semidefiniteness constraint on the Mahalanobis matrix, which results in a high computational cost. In this paper, we develop a novel approach to reduce this computational burden. We first map each training example into a new space by an orthonormal transformation. Then, in the transformed space, we simply learn a diagonal matrix. This two-phase approach is thus much easier and less costly than learning a full Mahalanobis matrix in one phase as is commonly done

The Role of Serial NT-ProBNP Level in Prognosis and Follow-Up Treatment of Acute Heart Failure after Coronary Artery Bypass Graft Surgery

BACKGROUND: After coronary artery bypass graft (CABG) surgery, heart failure is still major problem. The valuable marker for it is needed. AIM: Evaluating the role of serial NT-proBNP level in prognosis and follow-up treatment of acute heart failure after CABG surgery. METHODS: The prospective, analytic study evaluated 107 patients undergoing CABG surgery at Ho Chi Minh Heart Institute from October 2012 to June 2014. Collecting data was done at pre- and post-operative days with measuring NT-proBNP levels on the day before operation, 2 hours after surgery, every next 24 h until the 5th day, and in case of acute heart failure occurred after surgery. RESULTS: On the first postoperative day (POD1), the NT-proBNP level demonstrated significant value for AHF with the cut-off point = 817.8 pg/mL and AUC = 0.806. On the second and third postoperative day, the AUC value of NT- was 0.753 and 0.751. It was statistically significant in acute heart failure group almost at POD 1 and POD 2 when analyzed by the doses of dobutamine, noradrenaline, and adrenaline (both low doses and normal doses). CONCLUSION: Serial measurement of NT-proBNP level provides useful prognostic and follow-up treatment information in acute heart failure after CABG surgery

Antibiotic Resistance Profile and Methicillin-Resistant Encoding Genes of Staphylococcus aureus Strains Isolated from Bloodstream Infection Patients in Northern Vietnam

Background:  Evaluating the antibiotic susceptibility and resistance genes is essential in the clinical management of bloodstream infections (BSIs). Nevertheless, there are still limited studies in Northern Vietnam. AIM: This study aimed to determine the antibiotic resistance profile and methicillin-resistant encoding genes of Staphylococcus aureus (S. aureus) causing BSIs in Northern Vietnam. METHODS: The cross-sectional study was done from December 2012 to June 2014 in two tertiary hospitals in Northern Vietnam. Tests performed at the lab of the hospital. RESULTS:  In 43 S. aureus strains isolating, 53.5 % were MRSA. Distribution of gene for overall, MRSA, and MSSA strains were following: mecA gene (58.1 %; 95.7%, and 15%), femA gene (48.8%, 47.8%, and 50%), femB gene (88.4%, 82.6%, and 95%). Antibiotic resistance was highest in penicillin (100%), followed by erythromycin (65.1%) and clindamycin (60.5%). Several antibiotics were susceptible (100%), including vancomycin, tigecycline, linezolid, quinupristin/dalfopristin. Quinolone group was highly sensitive, include ciprofloxacin (83.7%), levofloxacin (86%) and moxifloxacin (86%). CONCLUSION:  In S. aureus causing BSIs, antibiotic resistance was higher in penicillin, erythromycin, and clindamycin. All strains were utterly susceptible to vancomycin, tigecycline, linezolid, quinupristin/dalfopristin

Antibiotic Resistance Profile and Diversity of Subtypes Genes in Escherichia coli Causing Bloodstream Infection in Northern Vietnam

BACKGROUND: Evaluating the antibiotic susceptibility and resistance genes is essential in the clinical management of bloodstream infections (BSIs). But there are still limited studies in Northern Vietnam. AIM: The aim of the study was to determine the antibiotic resistance profile and characteristics of subtypes genes in Escherichia coli causing BSIs in Northern Vietnam. METHODS: The cross-sectional study was done in the period from December 2012 to June 2014 in two tertiary hospitals in Northern Vietnam. Tests were performed at the lab of the hospital. RESULTS: In 56 E. coli strains isolating 39.29 % produced ESBL. 100% of the isolates harbored blaTEM gene, but none of them had the blaPER gene. The prevalence of ESBL producers and ESBL non-producers in blaCTX-M gene was 81.82%, and 73.53%, in blaSHV gene was 18.18% and 35.29%. Sequencing results showed three blaTEM subtypes (blaTEM 1, 79, 82), four blaCTX-M subtypes (blaCTX-M-15, 73, 98, 161), and eight blaSHV subtypes (blaSHV 5, 7, 12, 15, 24, 33, 57, 77). Antibiotic resistance was higher in ampicillin (85.71%), trimethoprim/sulfamethoxazole (64.29%) and cephazolin (50%). Antibiotics were still highly susceptible including doripenem (96.43%), ertapenem (94.64%), amikacin (96.43%), and cefepime (89.29%). CONCLUSION: In Escherichia coli causing BSIs, antibiotic resistance was higher in ampicillin, trimethoprim/sulfamethoxazole and cephazolin. Antibiotics was highly susceptible including doripenem, ertapenem, amikacin, and cefepime

Supervised distance metric learning for pattern recognition

Much like in other modeling disciplines does the distance metric used (a measure for dissimilarity) play an important role in the growing field of machine learning. Often, predefined distance metrics (e.g. the Euclidean one) are used to perform such measurement. Unfortunately, most of them ignore any statistical properties that might be estimated from the data. The notion of a good distance metric changes when one moves from one domain to another. For instance, in the problem of computing the dissimilarity for human images, two images could be considered as being similar due to one of the following reasons, the two images are taken from two persons with the same gender, the same age, or the same race. Clearly, it is difficult to use the same distance metric for gender, age, and race since two images might be similar in one case, while being dissimilar in the other case. For this reason, most research efforts have been devoted to automatically learn a good distance metric from data. Depending on the availability of training data, distance metric learning methods can be divided into three categories: supervised, semi-supervised, and unsupervised. Supervised methods often use the heuristic that examples belonging to the same class should be close to each other, while those from different classes should be farther apart. Semi-supervised methods use the information in the form of pairwise similarity or dissimilarity constraints. Unsupervised methods learn a distance metric that preserves the geometric relationships (i.e., distance) between most of the training data for the purpose of unsupervised dimensionality reduction. In this thesis, we focus on supervised distance metric learning. The main aim is to develop efficient and scalable algorithms for solving distance metric learning problems under different types of supervision. The proposed algorithms are supported by empirical as well as theoretical studies

An approach to supervised distance metric learning based on difference of convex functions programming

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Kernel-based distance metric learning for supervised k-means clustering

Finding an appropriate distance metric that accurately reflects the (dis)similarity between examples is a key to the success of k-means clustering. While it is not always an easy task to specify a good distance metric, we can try to learn one based on prior knowledge from some available clustered data sets, an approach that is referred to as supervised clustering. In this paper, a kernel-based distance metric learning method is developed to improve the practical use of k-means clustering. Given the corresponding optimization problem, we derive a meaningful Lagrange dual formulation and introduce an efficient algorithm in order to reduce the training complexity. Our formulation is simple to implement, allowing a large-scale distance metric learning problem to be solved in a computationally tractable way. Experimental results show that the proposed method yields more robust and better performances on synthetic as well as real-world data sets compared to other state-of-the-art distance metric learning methods

Kernel distance metric learning using pairwise constraints for person re-identification

Person re-identification is a fundamental task in many computer vision and image understanding systems. Due to appearance variations from different camera views, person re-identification still poses an important challenge. In the literature, KISSME has already been introduced as an effective distance metric learning method using pairwise constraints to improve the re-identification performance. Computationally, it only requires two inverse covariance matrix estimations. However, the linear transformation induced by KISSME is not powerful enough for more complex problems. We show that KISSME can be kernelized, resulting in a nonlinear transformation, which is suitable for many real-world applications. Moreover, the proposed kernel method can be used for learning distance metrics from structured objects without having a vectorial representation. The effectiveness of our method is validated on five publicly available data sets. To further apply the proposed kernel method efficiently when data are collected sequentially, we introduce a fast incremental version that learns a dissimilarity function in the feature space without estimating the inverse covariance matrices. The experiments show that the latter variant can obtain competitive results in a computationally efficient manner