Prediction by Nonparametric Posterior Estimation in Virtual Screening

The ability to rank molecules according to their effectiveness in some domain, e.g. pesticide, drug, is important owing to the cost of synthesising and testing chemical compounds. Virtual screening seeks to do this computationally with potential savings of millions of pounds and large profits associated with reduced time to market. Recently, binary kernel discrimination (BKD) is introduced and becoming popular in Chemoinformatics domain. It produces scores based on the estimated likelihood ratio of active to inactive compounds that are then ranked. The likelihoods are estimated through a Parzen Windows approach using the binomial distribution function (to accommodate binary descriptor or "fingerprint" vectors representing the presence, or not, of certain sub-structural arrangements of atoms) in place of the usual Gaussian choice. This research aims to compute the likelihood ratio via direct estimate of posterior probability by using non-parametric generalisation of logistic regression the so-called “Kernel Logistic Regression”. Furthermore, complexity is then controlled by penalising the likelihood function by Lq-norm. The compounds are then rank descending on the basis of posterior probability. The 11 activity classes from the MDL Drug Data Report (MDDR) database are used. The results are found to be less accurate than a currently leading approach but are still comparable in a number of cases

Learning relevant eye movement feature spaces across users

In this paper we predict the relevance of images based on a lowdimensional feature space found using several users’ eye movements. Each user is given an image-based search task, during which their eye movements are extracted using a Tobii eye tracker. The users also provide us with explicit feedback regarding the relevance of images. We demonstrate that by using a greedy Nystrom algorithm on the eye movement features of different users, we can find a suitable low-dimensional feature space for learning. We validate the suitability of this feature space by projecting the eye movement features of a new user into this space, training an online learning algorithm using these features, and showing that the number of mistakes (regret over time) made in predicting relevant images is lower than when using the original eye movement features. We also plot Recall-Precision and ROC curves, and use a sign test to verify the statistical significance of our results

New heuristic-based design of robust power system stabilizers

This paper proposes a new robust design of power system stabilizers (PSSs) in a multimachine power system using a heuristic optimization method. The structure of each PSS used is similar to that of a conventional lead/lag stabilizer. The proposed design regards a multimachine power system with PSSs as a multi-input multi-output (MIMO) control system. Additionally, a multiplicative uncertainty model is taken into account in the power system representation. Accordingly, the robust stability margin can be guaranteed by a multiplicative stability margin (MSM). The presented method utilizes the MSM as the design specification for robust stability. To acquire the control parameters of PSSs, a control design in MIMO system is formulated as an optimization problem. In the selection of objective function, not only disturbance attenuation performance but also robust stability indices are considered. Subsequently, the hybrid tabu search and evolutionary programming (hybrid TS/EP) is employed to search for the optimal parameters. The significant effects of designed PSSs are investigated under several system operating conditions

Power System Stabilizer Tuning Based on Multiobjective Design Using Hierarchical and Parallel Micro Genetic Algorithm

In order to achieve the optimal design based on some specific criteria by applying conventional techniques, sequence of design, selected locations of PSSs are critical involved factors. This paper presents a method to simultaneously tune PSSs in multimachine power system using hierarchical genetic algorithm (HGA) and parallel micro genetic algorithm (parallel micro-GA) based on multiobjective function comprising the damping ratio, damping factor and number of PSSs. First, the problem of selecting proper PSS parameters is converted to a simple multiobjective optimization problem. Then, the problem is solved by a parallel micro GA based on HGA. The stabilizers are tuned to simultaneously shift the lightly damped and undamped oscillation modes to a specific stable zone in the s-plane and to self identify the appropriate choice of PSS locations by using eigenvalue-based multiobjective function. Many scenarios with different operating conditions have been included in the process of simultaneous tuning so as to guarantee the robustness and their performance. A 68-bus and 16-generator power system has been employed to validate the effectiveness of the proposed tuning method

Ranking algorithms for implicit feedback

This report presents novel algorithms to use eye movements as an implicit relevance feedback in order to improve the performance of the searches. The algorithms are evaluated on "Transport Rank Five" Dataset which were previously collected in Task 8.3. We demonstrated that simple linear combination or tensor product of eye movement and image features can improve the retrieval accuracy

Sparse multinomial kernel discriminant analysis (sMKDA)

Dimensionality reduction via canonical variate analysis (CVA) is important for pattern recognition and has been extended variously to permit more flexibility, e.g. by "kernelizing" the formulation. This can lead to over-fitting, usually ameliorated by regularization. Here, a method for sparse, multinomial kernel discriminant analysis (sMKDA) is proposed, using a sparse basis to control complexity. It is based on the connection between CVA and least-squares, and uses forward selection via orthogonal least-squares to approximate a basis, generalizing a similar approach for binomial problems. Classification can be performed directly via minimum Mahalanobis distance in the canonical variates. sMKDA achieves state-of-the-art performance in terms of accuracy and sparseness on 11 benchmark datasets

A Simple Iterative Algorithm for Parsimonious Binary Kernel Fisher Discrimination

By applying recent results in optimization theory variously known as optimization transfer or majorize/minimize algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimization that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks

Image Ranking with Eye Movements

In order to help users navigate an image search system, one could provide explicit rank information on a set of images. These rankings are learnt so to present a new set of relevant images. Although, requiring explicit information may not be feasible in some cases, we consider the setting where the user provides implicit feedback, eye movements, to assist in such a task. This paper explores the idea of implicitly incorporating eye movement features in an image ranking task. Previous work had demonstrated that combining eye movement and image features improved the retrieval accuracy. Despite promising results the proposed approach is unrealistic as no eye movements are given a-priori for new images. We propose a novel search approach which combines image together with eye movements features in a tensor Ranking Support Vector Machine, and show that by extracting the individual source-specific weight vectors we are able to construct a new image-based semantic space which outperforms in retrieval accuracy