Relative Comparison Kernel Learning with Auxiliary Kernels

In this work we consider the problem of learning a positive semidefinite kernel matrix from relative comparisons of the form: "object A is more similar to object B than it is to C", where comparisons are given by humans. Existing solutions to this problem assume many comparisons are provided to learn a high quality kernel. However, this can be considered unrealistic for many real-world tasks since relative assessments require human input, which is often costly or difficult to obtain. Because of this, only a limited number of these comparisons may be provided. In this work, we explore methods for aiding the process of learning a kernel with the help of auxiliary kernels built from more easily extractable information regarding the relationships among objects. We propose a new kernel learning approach in which the target kernel is defined as a conic combination of auxiliary kernels and a kernel whose elements are learned directly. We formulate a convex optimization to solve for this target kernel that adds only minor overhead to methods that use no auxiliary information. Empirical results show that in the presence of few training relative comparisons, our method can learn kernels that generalize to more out-of-sample comparisons than methods that do not utilize auxiliary information, as well as similar methods that learn metrics over objects

Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations.

We present a novel method for the identification of sets of mutually exclusive gene alterations in a given set of genomic profiles. We scan the groups of genes with a common downstream effect on the signaling network, using a mutual exclusivity criterion that ensures that each gene in the group significantly contributes to the mutual exclusivity pattern. We test the method on all available TCGA cancer genomics datasets, and detect multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events

A two-step learning approach for solving full and almost full cold start problems in dyadic prediction

Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic prediction, a setting that occurs when both objects in an out-of-sample dyad have not been observed during training, or if one of them has been observed, but very few times. A popular approach for addressing this problem is to train a model that makes predictions based on a pairwise feature representation of the dyads, or, in case of kernel methods, based on a tensor product pairwise kernel. As an alternative to such a kernel approach, we introduce a novel two-step learning algorithm that borrows ideas from the fields of pairwise learning and spectral filtering. We show theoretically that the two-step method is very closely related to the tensor product kernel approach, and experimentally that it yields a slightly better predictive performance. Moreover, unlike existing tensor product kernel methods, the two-step method allows closed-form solutions for training and parameter selection via cross-validation estimates both in the full and almost full cold start settings, making the approach much more efficient and straightforward to implement

Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian inference, and a maximum-a-posteriori approach. The variational approach is new for the Bayesian nonnegative models. We compare their convergence, and robustness to noise and sparsity of the data, on both synthetic and real-world datasets. Furthermore, we extend the models with the Bayesian automatic relevance determination prior, allowing the models to perform automatic model selection, and demonstrate its efficiency

Fizik Öğretiminin Sorunları Üzerine Öğretmen Görüşlerinin Değerlendirilmesi

The aim of this study is to evaluate high school physics teachers’ opinions and suggestions about physics instruction. The respondents of the study consisted of 152 physics teachers working in the city centres of the provinces Diyarbakır, Şanlıurfa, Mardin, Sürt and Batman. The data were gathered through multiple- choice questioner posed to the teachers. The evaluation results are presented given as frequency distributions and percentages in charts and (ext. In the last section, some possible suggestions for physics instruction are made with the help of the results.Bu çalışmanın amacı lise fizik öğretmenlerinin fizik öğretiminin sorunlarına ilişkin görüş ve önerilerini değerlendirmektir. Araştırmanın örneklemi Diyarbakır, Şanlıurfa, Mardin, Siirt ve Batman il merkezlerinde görev yapan 152 fizik öğretmeni oluşturmaktadır. Veriler, öğretmenlere yöneltilen anket sorularına verilen yanıtlar ile elde edilmiştir.. Değerlendirme sonuçları, frekans dağılımları ve yüzdelikleri alınarak, tablolar ve metin içinde bulgular bölümünde sunulmuştur. Son bölümde fizik öğretimi için araştırma sonuçlarına dayalı alarak bazı öneriler verilmiştir

Free-hand sketch recognition by multi-kernel feature learning

Abstract Free-hand sketch recognition has become increasingly popular due to the recent expansion of portable touchscreen devices. However, the problem is non-trivial due to the complexity of internal structures that leads to intra-class variations, coupled with the sparsity in visual cues that results in inter-class ambiguities. In order to address the structural complexity, a novel structured representation for sketches is proposed to capture the holistic structure of a sketch. Moreover, to overcome the visual cue sparsity problem and therefore achieve state-of-the-art recognition performance, we propose a Multiple Kernel Learning (MKL) framework for sketch recognition, fusing several features common to sketches. We evaluate the performance of all the proposed techniques on the most diverse sketch dataset to date (Mathias et al., 2012), and offer detailed and systematic analyses of the performance of different features and representations, including a breakdown by sketch-super-category. Finally, we investigate the use of attributes as a high-level feature for sketches and show how this complements low-level features for improving recognition performance under the MKL framework, and consequently explore novel applications such as attribute-based retrieval

What Is the Important Point Related to Follow-Up Sonographic Evaluation for the Developmental Dysplasia of the Hip?

Developmental dysplasia of the hip (DDH) is an important cause of childhood disability. Subluxation or dislocation can be diagnosed through pediatric physical examination; nevertheless, the ultrasonographic examination is necessary in diagnosing certain borderline cases. It has been evaluated routine sonographic examination of 2,444 hips of 1,222 babies to determine differences in both, developmental dysplasia and types of hips, and evaluated their development on the 3-month follow-up. Evaluating the pathologic alpha angles under 59, there was no statistically significant differences between girls and boys in both right (55.57 +/- 3.73) (56.20 +/- 4.01), (p = 0.480), and left (55.79 +/- 3.96) (57.00 +/- 3.84), (p = 0.160) hips on the 45th day of life. Routine sonographic examinations on the 45th day of life revealed that 51 of (66.2%) 77 type 2a right hips were girls and 26 (33.8%) were boys. The number of the right hips that develop into type 1 was 38 (74.5%) for girls and 26 (100%) for boys on the 90th day of life (p = 0.005). A total of 87 type 2a left hips included 64 girls (73.6%) and 23 boys (26.4%). In the 90th day control, 49 right hip of girls (76.6%) and 21 right hip of boys (91.3%) developed into type 1 (p = 0.126). In the assessment of both left and right hips, girls showed a significantly higher frequency in latency and boys showed significantly higher development in the control sonography. A total of 31 girls (2.5%) and 11 boys (0.9%) accounted for a total of 42 (3.4%) cases who showed bilateral type 2a hips in 1,222 infants. On the 90th day control, 26 girls (83.9%) and all 11 boys (100%) developed into type 1 (p = 0.156). The study emphasizes the importance of the sonographic examination on the 90th day of life. Results of the investigation include the data of sonographic screening of DDH on the 45th day, and also stress the importance of the 90th-day control sonography after a close follow-up with physical examination between 45th and 90th days of life

Supervised selective kernel fusion for membrane protein prediction

Membrane protein prediction is a significant classification problem, requiring the integration of data derived from different sources such as protein sequences, gene expression, protein interactions etc. A generalized probabilistic approach for combining different data sources via supervised selective kernel fusion was proposed in our previous papers. It includes, as particular cases, SVM, Lasso SVM, Elastic Net SVM and others. In this paper we apply a further instantiation of this approach, the Supervised Selective Support Kernel SVM and demonstrate that the proposed approach achieves the top-rank position among the selective kernel fusion variants on benchmark data for membrane protein prediction. The method differs from the previous approaches in that it naturally derives a subset of “support kernels” (analogous to support objects within SVMs), thereby allowing the memory-efficient exclusion of significant numbers of irrelevant kernel matrixes from a decision rule in a manner particularly suited to membrane protein prediction

Kernelized Multiview Projection for Robust Action Recognition

Conventional action recognition algorithms adopt a single type of feature or a simple concatenation of multiple features. In this paper, we propose to better fuse and embed different feature representations for action recognition using a novel spectral coding algorithm called Kernelized Multiview Projection (KMP). Computing the kernel matrices from different features/views via time-sequential distance learning, KMP can encode different features with different weights to achieve a low-dimensional and semantically meaningful subspace where the distribution of each view is sufficiently smooth and discriminative. More crucially, KMP is linear for the reproducing kernel Hilbert space, which allows it to be competent for various practical applications. We demonstrate KMP’s performance for action recognition on five popular action datasets and the results are consistently superior to state-of-the-art techniques