Unsupervised Feature Selection with Adaptive Structure Learning

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods

Pengenalan Multi Wajah Berdasarkan Klasifikasi Kohonen SOM Dioptimalkan dengan Algoritma Discriminant Analysis PCA

Face recognition is a process of identification with the image has variations changeable can be recognized, needs a method of optimization to minimize computational time by not affecting the classification results. This research proposes a face recognition system are directly based on Kohonen SOM classification that optimized by the method of Discriminant Analysis based Principal Component Analysis (PCA). Evaluation of PCA’s extraction performance uses two approaches, first the LDA method to optimize PCA issues of the election of irrelevant features of the dataset and the second approach is to apply a kernel function on the LDA (KDA), the results of both approaches are applied on face image classification for Kohonen directly. The testing is two phases, the first stage is testing with a single image of a face and then multi face. Based on the results of testing one face image, both of the approached feature extraction that proposed is very accurately be applied to the classification of the Kohonen SOM with the accurate value of the second approach PCA-KDA is more accurate with 94.22% and the first approach 93.91%, however on the first approach is faster than the second approach with the accurate value of time 0.4 seconds for PCA-LDA and 0.5 seconds to PCA-KDA to one image of the face, but while testing of multi face more two images the result is not significant. Keywords: Face recognition, Feature extraction, Kohonen SOM

Open Set Chinese Character Recognition using Multi-typed Attributes

Recognition of Off-line Chinese characters is still a challenging problem, especially in historical documents, not only in the number of classes extremely large in comparison to contemporary image retrieval methods, but also new unseen classes can be expected under open learning conditions (even for CNN). Chinese character recognition with zero or a few training samples is a difficult problem and has not been studied yet. In this paper, we propose a new Chinese character recognition method by multi-type attributes, which are based on pronunciation, structure and radicals of Chinese characters, applied to character recognition in historical books. This intermediate attribute code has a strong advantage over the common `one-hot' class representation because it allows for understanding complex and unseen patterns symbolically using attributes. First, each character is represented by four groups of attribute types to cover a wide range of character possibilities: Pinyin label, layout structure, number of strokes, three different input methods such as Cangjie, Zhengma and Wubi, as well as a four-corner encoding method. A convolutional neural network (CNN) is trained to learn these attributes. Subsequently, characters can be easily recognized by these attributes using a distance metric and a complete lexicon that is encoded in attribute space. We evaluate the proposed method on two open data sets: printed Chinese character recognition for zero-shot learning, historical characters for few-shot learning and a closed set: handwritten Chinese characters. Experimental results show a good general classification of seen classes but also a very promising generalization ability to unseen characters.Comment: 29 pages, submitted to Pattern Recognitio

Pengenalan Multi Wajah Berdasarkan Klasifikasi Kohonen SOM Dioptimalkan dengan Algoritma Discriminant Analysis PCA

Face recognition is a process of identification with the image has variations changeable can be recognized, needs a method of optimization to minimize computational time by not affecting the classification results. This research proposes a face recognition system are directly based on Kohonen SOM classification that optimized by the method of Discriminant Analysis based Principal Component Analysis (PCA). Evaluation of PCA’s extraction performance uses two approaches, first the LDA method to optimize PCA issues of the election of irrelevant features of the dataset and the second approach is to apply a kernel function on the LDA (KDA), the results of both approaches are applied on face image classification for Kohonen directly. The testing is two phases, the first stage is testing with a single image of a face and then multi face. Based on the results of testing one face image, both of the approached feature extraction that proposed is very accurately be applied to the classification of the Kohonen SOM with the accurate value of the second approach PCA-KDA is more accurate with 94.22% and the first approach 93.91%, however on the first approach is faster than the second approach with the accurate value of time 0.4 seconds for PCA-LDA and 0.5 seconds to PCA-KDA to one image of the face, but while testing of multi face more two images the result is not significant. Keywords: Face recognition, Feature extraction, Kohonen SOM

Deep Learning Architectures for Novel Problems

With convolutional neural networks revolutionizing the computer vision field it is important to extend the capabilities of neural-based systems to dynamic and unrestricted data like graphs. Doing so not only expands the applications of such systems, but also provide more insight into improvements to neural-based systems. Currently most implementations of graph neural networks are based on vertex filtering on fixed adjacency matrices. Although important for a lot of applications, vertex filtering restricts the applications to vertex focused graphs and cannot be efficiently extended to edge focused graphs like social networks. Applications of current systems are mostly limited to images and document references. Beyond the graph applications, this work also explored the usage of convolutional neural networks for intelligent character recognition in a novel way. Most systems define Intelligent Character Recognition as either a recurrent classification problem or image classification. This achieves great performance in a limited environment but does not generalize well on real world applications. This work defines intelligent Character Recognition as a segmentation problem which we show to provide many benefits. The goal of this work was to explore alternatives to current graph neural networks implementations as well as exploring new applications of such system. This work also focused on improving Intelligent Character Recognition techniques on isolated words using deep learning techniques. Due to the contrast between these to contributions this documents was divided into Part I focusing on the graph work, and Part II focusing on the intelligent character recognition work