A Novel Two-Stage Spectrum-Based Approach for Dimensionality Reduction: A Case Study on the Recognition of Handwritten Numerals

Dimensionality reduction (feature selection) is an important step in pattern recognition systems. Although there are different conventional approaches for feature selection, such as Principal Component Analysis, Random Projection, and Linear Discriminant Analysis, selecting optimal, effective, and robust features is usually a difficult task. In this paper, a new two-stage approach for dimensionality reduction is proposed. This method is based on one-dimensional and two-dimensional spectrum diagrams of standard deviation and minimum to maximum distributions for initial feature vector elements. The proposed algorithm is validated in an OCR application, by using two big standard benchmark handwritten OCR datasets, MNIST and Hoda. In the beginning, a 133-element feature vector was selected from the most used features, proposed in the literature. Finally, the size of initial feature vector was reduced from 100% to 59.40% (79 elements) for the MNIST dataset, and to 43.61% (58 elements) for the Hoda dataset, in order. Meanwhile, the accuracies of OCR systems are enhanced 2.95% for the MNIST dataset, and 4.71% for the Hoda dataset. The achieved results show an improvement in the precision of the system in comparison to the rival approaches, Principal Component Analysis and Random Projection. The proposed technique can also be useful for generating decision rules in a pattern recognition system using rule-based classifiers

Emergence of number sense through the integration of multimodal information: developmental learning insights from neural network models

IntroductionAssociating multimodal information is essential for human cognitive abilities including mathematical skills. Multimodal learning has also attracted attention in the field of machine learning, and it has been suggested that the acquisition of better latent representation plays an important role in enhancing task performance. This study aimed to explore the impact of multimodal learning on representation, and to understand the relationship between multimodal representation and the development of mathematical skills.MethodsWe employed a multimodal deep neural network as the computational model for multimodal associations in the brain. We compared the representations of numerical information, that is, handwritten digits and images containing a variable number of geometric figures learned through single- and multimodal methods. Next, we evaluated whether these representations were beneficial for downstream arithmetic tasks.ResultsMultimodal training produced better latent representation in terms of clustering quality, which is consistent with previous findings on multimodal learning in deep neural networks. Moreover, the representations learned using multimodal information exhibited superior performance in arithmetic tasks.DiscussionOur novel findings experimentally demonstrate that changes in acquired latent representations through multimodal association learning are directly related to cognitive functions, including mathematical skills. This supports the possibility that multimodal learning using deep neural network models may offer novel insights into higher cognitive functions

Reliable pattern recognition system with novel semi-supervised learning approach

Over the past decade, there has been considerable progress in the design of statistical machine learning strategies, including Semi-Supervised Learning (SSL) approaches. However, researchers still have difficulties in applying most of these learning strategies when two or more classes overlap, and/or when each class has a bimodal/multimodal distribution. In this thesis, an efficient, robust, and reliable recognition system with a novel SSL scheme has been developed to overcome overlapping problems between two classes and bimodal distribution within each class. This system was based on the nature of category learning and recognition to enhance the system's performance in relevant applications. In the training procedure, besides the supervised learning strategy, the unsupervised learning approach was applied to retrieve the "extra information" that could not be obtained from the images themselves. This approach was very helpful for the classification between two confusing classes. In this SSL scheme, both the training data and the test data were utilized in the final classification. In this thesis, the design of a promising supervised learning model with advanced state-of-the-art technologies is firstly presented, and a novel rejection measurement for verification of rejected samples, namely Linear Discriminant Analysis Measurement (LDAM), is defined. Experiments on CENPARMI's Hindu-Arabic Handwritten Numeral Database, CENPARMI's Numerals Database, and NIST's Numerals Database were conducted in order to evaluate the efficiency of LDAM. Moreover, multiple verification modules, including a Writing Style Verification (WSV) module, have been developed according to four newly defined error categories. The error categorization was based on the different costs of misclassification. The WSV module has been developed by the unsupervised learning approach to automatically retrieve the person's writing styles so that the rejected samples can be classified and verified accordingly. As a result, errors on CENPARMI's Hindu-Arabic Handwritten Numeral Database (24,784 training samples, 6,199 testing samples) were reduced drastically from 397 to 59, and the final recognition rate of this HAHNR reached 99.05%, a significantly higher rate compared to other experiments on the same database. When the rejection option was applied on this database, the recognition rate, error rate, and reliability were 97.89%, 0.63%, and 99.28%, respectivel

Design for novel enhanced weightless neural network and multi-classifier.

Weightless neural systems have often struggles in terms of speed, performances, and memory issues. There is also lack of sufficient interfacing of weightless neural systems to others systems. Addressing these issues motivates and forms the aims and objectives of this thesis. In addressing these issues, algorithms are formulated, classifiers, and multi-classifiers are designed, and hardware design of classifier are also reported. Specifically, the purpose of this thesis is to report on the algorithms and designs of weightless neural systems. A background material for the research is a weightless neural network known as Probabilistic Convergent Network (PCN). By introducing two new and different interfacing method, the word "Enhanced" is added to PCN thereby giving it the name Enhanced Probabilistic Convergent Network (EPCN). To solve the problem of speed and performances when large-class databases are employed in data analysis, multi-classifiers are designed whose composition vary depending on problem complexity. It also leads to the introduction of a novel gating function with application of EPCN as an intelligent combiner. For databases which are not very large, single classifiers suffices. Speed and ease of application in adverse condition were considered as improvement which has led to the design of EPCN in hardware. A novel hashing function is implemented and tested on hardware-based EPCN. Results obtained have indicated the utility of employing weightless neural systems. The results obtained also indicate significant new possible areas of application of weightless neural systems

Enhancing Face Recognition with Deep Learning Architectures: A Comprehensive Review

The progression of information discernment via facial identification and the emergence of innovative frameworks has exhibited remarkable strides in recent years. This phenomenon has been particularly pronounced within the realm of verifying individual credentials, a practice prominently harnessed by law enforcement agencies to advance the field of forensic science. A multitude of scholarly endeavors have been dedicated to the application of deep learning techniques within machine learning models. These endeavors aim to facilitate the extraction of distinctive features and subsequent classification, thereby elevating the precision of unique individual recognition. In the context of this scholarly inquiry, the focal point resides in the exploration of deep learning methodologies tailored for the realm of facial recognition and its subsequent matching processes. This exploration centers on the augmentation of accuracy through the meticulous process of training models with expansive datasets. Within the confines of this research paper, a comprehensive survey is conducted, encompassing an array of diverse strategies utilized in facial recognition. This survey, in turn, delves into the intricacies and challenges that underlie the intricate field of facial recognition within imagery analysis