Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach

Feature selection is playing an increasingly significant role with respect to many computer vision applications spanning from object recognition to visual object tracking. However, most of the recent solutions in feature selection are not robust across different and heterogeneous set of data. In this paper, we address this issue proposing a robust probabilistic latent graph-based feature selection algorithm that performs the ranking step while considering all the possible subsets of features, as paths on a graph, bypassing the combinatorial problem analytically. An appealing characteristic of the approach is that it aims to discover an abstraction behind low-level sensory data, that is, relevancy. Relevancy is modelled as a latent variable in a PLSA-inspired generative process that allows the investigation of the importance of a feature when injected into an arbitrary set of cues. The proposed method has been tested on ten diverse benchmarks, and compared against eleven state of the art feature selection methods. Results show that the proposed approach attains the highest performance levels across many different scenarios and difficulties, thereby confirming its strong robustness while setting a new state of the art in feature selection domain.Comment: Accepted at the IEEE International Conference on Computer Vision (ICCV), 2017, Venice. Preprint cop

Context Trees: Augmenting Geospatial Trajectories with Context

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context tree construction that augments geospatial trajectories with land usage data to identify such contexts. Through evaluation of the construction method and analysis of the properties of generated context trees, we demonstrate the foundation for understanding and modelling behaviour afforded. Summarising user contexts into a single data structure gives easy access to information that would otherwise remain latent, providing the basis for better understanding and predicting the actions and behaviours of individuals and groups. Finally, we also present a method for pruning context trees, for use in applications where it is desirable to reduce the size of the tree while retaining useful information

Adaptive Data Mining Approach for Pcb Defect Detection and Classification

Objective: To develop a model for PCB defect detection and classification with the help of soft computing technique. Methodology: To improve the performance of the prediction and classification we propose a hybrid approach for feature reduction and classification. The proposed approach is divided into three main stages: (i) data pre-processing (ii) feature selection and reduction and (iii) Classification. In this approach, pre-processing, feature selection and reduction is carried out by measuring of confidence with the adaptive genetic algorithm. Prediction and classification is carried out by using neural network classifier. A genetic algorithm is used for data preprocessing to achieve the feature reduction and confidence measurement. Findings: The system is implemented using MatLab 2013b. The resulting analysis shows that the proposed approach is capable of detecting and classifying defects in PCB board

Hybrid classification approach hdlmm for learning disability prediction in school going children using data mining technique

Learning Disability is a disorder of neurological condition which causes deficiency in child�s brain activities such as reading, speaking and many other tasks. According to the World Health Organization (WHO), 15 of the children get affected by the learning disability. Efficient prediction and accurate classification is the crucial task for researchers for early detection of learning disability. In this work, our main aim to develop a model for learning disability prediction and classification with the help of soft computing technique. To improve the performance of the prediction and classification we propose a hybrid approach for feature reduction and classification. Proposed approach is divided into three main stages: (i) data pre-processing (ii) feature selection and reduction and (iii) Classification. In this approach, preprocessing, feature selection and reduction is carried out by measuring of confidence with adaptive genetic algorithm. Prediction and classification is carried out by using Deep Learner Neural network and Markov Model. Genetic algorithm is used for data preprocessing to achieve the feature reduction and confidence measurement. The system is implemented using MatLab 2013b. Result analysis shows that the proposed approach is capable to predict the learning disability effectively. © 2005 � ongoing JATIT & LLS