Face Recognition Using Self-Organizing Maps

Chen, Qiu; Kotani, Kazuhiko; Lee, Feifei; Ohmi, Tadahiro

doi:10.5772/9173

Cited by 11 publications

(3 citation statements)

References 40 publications

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“…Where the dimensionality of the data is very high and the number of objects makes classification difficult, the SOM first selects inputs in a random way, computes winner neurons (the most distinctive nodes/features), updates them, and repeats the process for all input data (Kohonen, 2012). The SOM thus provides an orderly mapping of a high dimensional space into much lower dimensional spaces, leading to dimension reduction and feature extraction for better classification performance (Chen, Lee, Kotani, & Ohmi, 2010). In this study, the high dimensions of the data were reduced through a process that produces winning nodes.…”

Section: Discussionmentioning

confidence: 99%

Towards a Linguistic Stylometric Model for the Authorship Detection in Cybercrime Investigations

Omar¹,

Deraan²

2019

IJEL

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This study proposes an integrated framework that considers letter-pair frequencies/combinations along with the lexical features of documents as a means to identifying the authorship of short texts posted anonymously on social media. Taking a quantitative morpho-lexical approach, this study tests the hypothesis that letter information, or mapping, can identify unique stylistic features. As such, stable word combinations and morphological patterns can be used successfully for authorship detection in relation to very short texts. This method offers significant potential in the fight against online hate speech, which is often posted anonymously and where authorship is difficult to identify. The data analyzed is from a corpus of 12,240 tweets derived from 87 Twitter accounts. A self-organizing map (SOM) model was used to classify input patterns in the tweets that shared common features. Tweets grouped in a particular class displayed features that suggested they were written by a particular author. The results indicate that the accuracy of classification according to the proposed system was around 76%. Up to 22% of this accuracy was lost, however, when only distinctive words were used and 26% was lost when the classification procedure was based solely on letter combinations and morphological patterns. The integration of letter-pairs and morphological patterns had the advantage of improving accuracy when determining the author of a given tweet. This indicates that the integration of different linguistic variables into an integrated system leads to better performance in classifying very short texts. It is also clear that the use of a self-organizing map (SOM) led to better clustering performance because of its capacity to integrate two different linguistic levels for each author profile.

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Section: Discussionmentioning

confidence: 99%

Towards a Linguistic Stylometric Model for the Authorship Detection in Cybercrime Investigations

Omar¹,

Deraan²

2019

IJEL

View full text Add to dashboard Cite

show abstract

“…The SOM thus provides an orderly mapping of a high dimensional space into much lower dimensional spaces, leading to dimension reduction and feature extraction for better classification performance (Q. Chen, Lee, Kotani, & Ohmi, 2010). In this study, the high dimensions of the data were reduced through a process that produces winning nodes.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Semantic Mapping Approach to the Genre Classification of Literature The American Novel as an Example

Omar

2019

مجلة البحث العلمی فی الآداب

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One of the limitations of genre classification of literature is that it is either based on philological methods or standard automatic text classification (ATC) methods. Moreover, a gap between literary analysis and computational theory further highlights this limitation. In order to bridge this gap, this study discusses a genre classification approach based on topic concepts rather than topic words where texts are represented using the Bag of Concepts (BOC) model due to the rich knowledge sources and meaningful relations concepts have. Texts were classified using a hybrid model of explicit semantic analysis (ESA) and ConceptNet. Results indicate that the proposed model led to better classification performance in relation to describing the connections between the document-members of each cluster and making generalizations about their unifying genre. The implications of the study are also useful for digital libraries and archives whose classifications of literary texts are in many cases misleading to readers and users.

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“…The SOM thus provides an orderly mapping of an input high dimensional space in much lower dimensional spaces, so it can play the role of dimension reduction and feature extraction for better classification performance (Q. Chen, Lee, Kotani, & Ohmi, 2010). In the case of the present study, the high dimensions of the data were reduced through a process known as the winning nodes.…”

Section: Applicationmentioning

confidence: 99%

Cybercrime and Authorship Detection in Very Short Texts A Quantitative Morpho-lexical Approach

Omar

2019

مجلة البحث العلمی فی الآداب

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The present study proposes an integrated framework that considers letterpair frequencies/combinations along with the lexical features of documents. Drawing on a quantitative morpho-lexical approach, the study ‫لسنة‬ ‫العشرون‬ ‫العدد‬ ‫اآلداب‬ ‫فى‬ ‫العلمى‬ ‫البحث‬ ‫مجلة‬ 9102 ‫األول‬ ‫الجزء‬ tests the hypothesis that letter information or mapping carries unique stylistic features; and therefore detecting stable word combinations and morphological patterns can be used to enhance the authorship performance in relation to very short texts. The data used for analysis is a corpus of 12240 tweets derived from 87 Twitter accounts. Self-organizing maps (SOMs) model is used for classifying the input patterns that share common features together as a clue that tweets grouped under one class membership are written by the same author. Results indicate that the classification accuracy based on the proposed system is around 76%. Up to 22% of this accuracy was lost, however, when only distinctive words were used, and 26% was lost when the classification performance was based on letter combinations and morphological patterns only. The integration of letter-pairs and morphological patterns had the advantage of improving the accuracy of determining the author of a given tweet. This indicates that the integration of different linguistic variables into an integrated system leads to a better classification performance of very short texts. It is also clear that the use of the self-organizing map (SOM) led to better clustering performance for its capacity to integrate two different linguistic levels of each author profile together.

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Face Recognition Using Self-Organizing Maps

Cited by 11 publications

References 40 publications

Towards a Linguistic Stylometric Model for the Authorship Detection in Cybercrime Investigations

Towards a Linguistic Stylometric Model for the Authorship Detection in Cybercrime Investigations

A Hybrid Semantic Mapping Approach to the Genre Classification of Literature The American Novel as an Example

Cybercrime and Authorship Detection in Very Short Texts A Quantitative Morpho-lexical Approach

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