Sparse Support Matrix Machines for the Classification of Corrupted Data

University of Technology Sydney. Faculty of Engineering and Information Technology.Support matrix machine is fragile to the presence of outliers: even few corrupted data points can arbitrarily alter the quality of the approximation, What if a fraction of columns are corrupted? In real world, the data is noisy and most of the features may be redundant as well as may be useless, which in turn affect the classification performance. Thus, it is important to perform robust feature selection under robust metric learning to filter out redundant features and ignore the noisy data points for more interpretable modelling. To overcome this challenge, in this work, we propose a new model to address the classification problem of high dimensionality data by jointly optimizing the both regularizer and hinge loss. We combine the hinge loss and regularization terms as spectral elastic net penalty. The regularization term which promotes the structural sparsity and shares similar sparsity patterns across multiple predictors. It is a spectral extension of the conventional elastic net that combines the property of low-rank and joint sparsity together, to deal with complex high dimensional noisy data. We further extends this approach by combining the recovery along with feature selection and classification could significantly improve the performance based on the assumption that the data consists of a low rank clean matrix plus a sparse noise matrix. We perform matrix recovery, feature selection and classification through joint minimization of p,q-norm and nuclear norm under the incoherence and ambiguity conditions and able to recover intrinsic matrix of higher rank and recover data with much denser corruption. Although, above both methods takes full advantage of low rank assumption to exploit the strong correlation between columns and rows of each matrix and able to extract useful features, however, are originally built for binary classification problems. To improve the robustness against data that is rich in outliers, we further extend this problem and present a novel multiclass support matrix machine by utilizing the maximization of the inter-class margins (i.e. margins between pairs of classes). We demonstrate the significance and advantage of our methods on different available benchmark datasets such as person identification, face recognition and EEG classification. Results showed that our methods achieved significantly better performance both in terms of time and accuracy for solving the classification problem of highly correlated matrix data as compared to state-of-the-art methods

Sub-sampling Approach for Unconstrained Arabic Scene Text Analysis by Implicit Segmentation based Deep Learning Classifier

The text extraction from the natural scene image is still a cumbersome task to perform. This paper presents a novel contribution and suggests the solution for cursive scene text analysis notably recognition of Arabic scene text appeared in the unconstrained environment. The hierarchical sub-sampling technique is adapted to investigate the potential through sub-sampling the window size of the given scene text sample. The deep learning architecture is presented by considering the complexity of the Arabic script. The conducted experiments present 96.81% accuracy at the character level. The comparison of the Arabic scene text with handwritten and printed data is outlined as well

Multi-font Numerals Recognition for Urdu Script based Languages

International audienceHandwritten character recognition of Urdu script based languages is one of the most difficult task due to complexities of the script. Urdu script based languages has not received much attestation even this script is used more than 1/6th of the population. The complexities in the script makes more complicated the recognition process. The problem in handwritten numeral recognition is the shape similarity between handwritten numerals and dual style for Urdu. This paper presents a fuzzy rule base, HMM and Hybrid approaches for the recognition of numerals both Urdu and Arabic in unconstrained environment from both online and offline domain for online input. Basically offline domain is used for preprocessing i.e normalization, slant normalization. The proposed system is tested and provides accuracy of 97.1

Applying deep neural networks for user intention identification

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations in identifying user’s purchase intentions. The prior works have focused on using only the CNN model to perform the feature extraction without retaining the sequence correlation. Moreover, many recent studies have applied classical feature representation techniques followed by a machine learning classifier. We examine the intention review identification problem using a deep learning model with an emphasis on maintaining the sequence correlation and also to retain information for a long time span. The proposed method consists of the convolutional neural network along with long short-term memory for efficient detection of intention in a given review, i.e., whether the review is an intent vs non-intent. The experimental results depict that the performance of the proposed system is better with respect to the baseline techniques with an accuracy of 92% for Dataset1 and 94% for Dataset2. Moreover, statistical analysis also depicts the effectiveness of the proposed method with respect to the comparing methods

Multimodal face and finger veins biometric authentication

Due to the increase in security requirements, biometric systems have been commonly utilized in many recognition applications. Multimodal has great demands to overcome the issue involved in single trait system and it has become one of the most important research areas of pattern recognition. We present multimodal face and finger veins biometric verification system to improve the performance. We presented multilevel score fusion of face and finger veins to provide better accuracy. Simulation results shows that proposed multimodal recognition system is very efficient to reduce the false rejection rate

Multimodal biometric recognition based on fusion of low resolution face and finger veins

Multimodal biometric systems utilize multiple biometric sources in order to increase robustness as compared to single biometric system. Most of the biometric systems in real are single or multimodal authentication system. This paper presents an efficient multimodal low resolution face and finger veins biometric recognition system based on class specific liner discriminant to client specific discriminant analysis and finger veins fusion at score level. Simulation results show that the proposed multimodal recognition system is very efficient to reduce the FAR and increase GAR, but it is more computationally complex due to processing involved in layered computation of LDA and CSLDA at runtime

Handwriting dynamics assessment using deep neural network for early identification of Parkinson\u27s disease

The etiology of Parkinson's disease (PD) remains unclear. Symptoms usually appear after approximately 70% of dopamine-producing cells have stopped working normally. PD cannot be cured, but its symptoms can be managed to delay its progression. Evidence suggests that early diagnosis is important in establishing an effective pathway for management of symptoms. However, PD diagnosis is challenging, particularly in the early stages of the disease. In this paper, we present a method for early diagnosis of PD using patients’ handwriting samples. To improve performance, we combined multiple PD handwriting datasets and used deep transfer learning-based algorithms to overcome the challenge of high variability in the handwritten material. Our approach achieved excellent PD identification performance with 99.22% accuracy on illuminated task of combined HandPD, NewHandPD and Parkinson's Drawing datasets, demonstrating the superiority of our approach over current state-of-the-art methods. © 2020 Elsevier B.V