Uncorrelated Local Maximum Margin Criterion: An Efficient Dimensionality Reduction Method for Text Classification

Local graph embedding based on maximum margin criterion via fuzzy set

The problem of ultra-high dimensionality small samples exists in the face data. In order to obtain discriminative features with higher generalization ability and improve the face recognition rate, a weighted maximum margin criterion (WMMC) method is presented for extracting discriminative features of face data. The method considers that the actual inter-class scatter is less than the inter-class scatter reflected by the inter-class scatter matrix calculated with limited training samples, and the actual intra-class scatter is more than the intra-class scatter reflected by the intra-class scatter matrix calculated with limited training samples, the WMMC function is obtained by recalculating the inter-class scatter matrix and intra-class scatter matrix. Experimental results using minimum distance (MD) and k-nearest neighbor (kNN) classifiers on ORL and Yale face datasets show that WMMC extracts discriminative features with higher generalization ability and significantly improves face recognition rate compared to maximum margin criterion (MMC).KeywordsFeature extractionWeighted maximum margin criterion (WMMC)Linear discriminant analysis (LDA)Face recognition

In order to search for an efficient feature extraction approach for face recognition,a method based on feature level fusion was presented.This method integrated Locality Preserving Projection(LPP) with Maximum Margin Criterion(MMC).Firstly,LPP was performed on training samples set,so the projection of each training sample on LPP subspace could be got.Further,MMC algorithm was performed on all the obtained projections to get more efficient discriminant features for recognition.Nearest Neighborhood(NN) algorithm was used to construct classifiers.The experiments on the ORL face database show that the recognition rate of the proposed method is high when pose,illumination condition,face expression and training sample number change.

Linear discriminant analysis (LDA) is a popular feature extraction technique in statistical pattern recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional image data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper, a new feature extraction method, nonparametric maximum margin criterion (NMMC), is proposed. NMMC finds the important discriminant directions without assuming the class densities belong to any particular parametric family, and it does not depend on the nonsingularity of the within-class scatter matrix. Our experimental results on the ATT and FERET face databases demonstrate that NMMC outperforms the existing variant LDA methods and the other state-of-art face recognition approaches.

Using face annotation for effective management of personal photos in online social networks (OSNs) is currently of considerable practical interest. In this paper, we propose a novel collaborative face recognition (FR) framework, improving the accuracy of face annotation by effectively making use of multiple FR engines available in an OSN. Our collaborative FR framework consists of two major parts: selection of FR engines and merging (or fusion) of multiple FR results. The selection of FR engines aims at determining a set of personalized FR engines that are suitable for recognizing query face images belonging to a particular member of the OSN. For this purpose, we exploit both social network context in an OSN and social context in personal photo collections. In addition, to take advantage of the availability of multiple FR results retrieved from the selected FR engines, we devise two effective solutions for merging FR results, adopting traditional techniques for combining multiple classifier results. Experiments were conducted using 547 991 personal photos collected from an existing OSN. Our results demonstrate that the proposed collaborative FR method is able to significantly improve the accuracy of face annotation, compared to conventional FR approaches that only make use of a single FR engine. Further, we demonstrate that our collaborative FR framework has a low computational cost and comes with a design that is suited for deployment in a decentralized OSN.

In this paper, a novel Gabor-2DFisherface approach with selecting 2D Gabor principal components and discriminant vectors is proposed for face recognition. Gabor transform is an important frequency-domain analysis tool. The proposed approach combines it with discriminant analysis technique. This approach first preprocesses all image samples by using Gabor transform, and then calculates 2D Gabor principal components and discriminant vectors by using 2DFisherface method. To enhance the discriminant capability, an automatic strategy is employed to select these components and vectors. After extracting the discriminant features, this approach adopts the nearest neighbor classifier with cosine distance for classification. The experimental results on the public AR face database demonstrate that the proposed approach outperforms several related discrimination methods.

Stemming is a fundamental step in processing textual data preceding the tasks of text mining, Information Retrieval (IR), and natural language processing (NLP). The common goal of stemming is to standardize words by reducing a word to its base (root or stem), thus can be also considered a feature reduction technique. This paper aims at presenting a new dictionary free, content-based Arabic stemmer and adopts it as a feature reduction (selection) mechanism to study its contribution in improving Arabic text categorization. We employed three stemming mechanisms (root-based, light, and our stemming technique and assessed their performance in text classification exercises for an Arabic corpus to compare and contrast the text mining effectiveness of these Arabic stemming algorithms. The experiments were conducted on a corpus consisting of 2,966 Arabic documents that fall into three categories: cultural, social, and general. The experiment results showed that our stemmer significantly improved text classification accuracy.

Taking advantage of the large scale corpus on the web to effectively and efficiently mine the topics within texts is an essential problem in the era of big data. We focus on the problem of learning text topic embedding in an unsupervised manner, which enjoys the properties of efficiency and scalability. Text topic embedding represents words and documents in a semantic topic space, in which the words and documents with similar topic will be embedded close to each other. When compared with conventional topic models, which implicitly capture the document-level word co-occurrence patterns, text topic embedding alleviates the data sparsity problem and captures the semantic relevance between different words and documents. To model text topic embedding, we propose a Bidirectional Hierarchical Skip-Gram model (BHSG) based on skip-gram model. BHSG includes two components: semantic generation module to learn semantic relevance between texts and topic enhance module to produce the text topic embedding based on text embedding learned in the former module. We evaluated our method on two kinds of topic-related tasks: text classification and information retrieval. The experimental results on four public datasets and one dataset we provide all demonstrate that our proposed method can achieve a better performance.

Texts classification is an important field that can be used in data mining, information retrieval, machine learning. Documents classification now widely used in different domains, such as mail spam filtering, article indexing, Web searching, and Web page categorization. There are many researches in documents classification for English language, but a few research in Arabic language, while there are large community in the world that uses this language. This paper analyzes the effect of preprocessing, such as tokenization, normalization and removing stop words, stemming using Khoja stemmer, stemming using light stemmer, stemming using Khoja stemmer with tokenization, normalization and removing stop words, and stemming using light stemmer with tokenization, normalization and removing stop words on documents classification. This study uses three classification algorithms, Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM), it is applied on online Arabic corpus prepared by Diab Abuaiadh. Rapid Miner tool is used to apply the three classification models. Whereas the condition of the documents before preprocessing is compared with their condition after preprocessing to determine the extent of the effect of preprocessing on documents classification. the results demonstrate variation between document classification before preprocessing and after preprocessing, and difference between the three algorithms in terms of Accuracy, Precision, Recall, and F1-Score, whereas it will be discussed later.

A novel unsupervised corpus-based stemming technique using lexicon and corpus statistics

The data mining is a task of analyzing the data and their hidden patterns. Therefore the classification and clustering approaches are used for performing this task. The classification of the data needs to be performing training over the similar patterns and using the training knowledge the upcoming patterns are recognized. In the data mining the text analysis is one of the popular aspects of the data mining. In this presented work the text analysis and their classification techniques are studied. In addition of that a new text classification approach is introduced for analysing the social media text. The proposed technique helps for automatically analysis of the text patterns and obtaining their positive and negative orientation according to the learned patterns. The proposed technique utilizes the word probability, sentence formation probability and the weighted indexing of the data for recognizing the orientation of the text data. The proposed technique organized in such manner by which the different combinations of the words can be analysed using the graphical approach. Therefore the computed probability and weights are used for generating the directed weighted graphs. Using this graph the upcoming technique is classified and their orientation is obtained. The implementation of the proposed text classification data model is provided using JAVA technology and their performance is also evaluated. The performance of the system is reported in terms accuracy, error rate, memory usage and the time complexity. According to the different experimental analysis the proposed technique is found accurate and efficient for text classification. Additionally that can also be extendable for finding the classes more than two classes.

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.

Small sample size is one of the most challenging problems in face recognition due to the difficulty of sample collection in many real-world applications. By representing the query sample as a linear combination of training samples from all classes, the so-called collaborative representation based classification (CRC) shows very effective face recognition performance with low computational cost. However, the recognition rate of CRC will drop dramatically when the available training samples per subject are very limited. One intuitive solution to this problem is operating CRC on patches and combining the recognition outputs of all patches. Nonetheless, the setting of patch size is a non-trivial task. Considering the fact that patches on different scales can have complementary information for classification, we propose a multi-scale patch based CRC method, while the ensemble of multi-scale outputs is achieved by regularized margin distribution optimization. Our extensive experiments validated that the proposed method outperforms many state-of-the-art patch based face recognition algorithms.

Word vector is an important tool for natural language processing (NLP) tasks such as text classification. However, existing static language models such as Word2vec cannot solve the polysemy problem, leading to a decline in text classification performance. To solve this problem, this paper proposes a method for making Chinese word vector dynamic (MCWVD). The part of speech (POS) is used to solve the ambiguity problem caused by different POS. The POS structure graph is constructed and the syntactic structure information of POS features is extracted by GCN (Graph Convolutional Network). POS vector and word vector are concatenated into PW (POS-Word) vector. Parametric matrix is added to improve the fusion effect of POS and word features. Multilayer attention is used to distinguish the importance of different features and further update the vector expression of word vectors about the current context. Experiments on Chinese datasets THUCNews and SogouNews show that MCWVD effectively improves the accuracy of text classification and achieves better performance than CoVe (Context Vectors) and ELMo (Embeddings from Language Models). MCWVD also achieves similar performance to BERT and GPT-1 (Generative Pre-Training), but with a much lower computational cost and only 4% of BERT parameters.

Text mining and data mining are contrasted relative to automated prediction. Models are constructed by training on samples of unstructured documents, and results are projected to new text. A standard data format for input to prediction methods is described. The key objective of data preparation is to transform text into a numerical format, eventually sharing a common representation with numerical data mining. Different text-mining tasks are introduced that fit within a predictive framework for machine-learning. These include document classification, information retrieval, clustering of documents, information extraction, and performance evaluation.