مروری بر روش‌های انتخاب ویژگی نیمه‌نظارتی تُنک مبتنی بر گراف

نوع مقاله : مقاله پژوهشی

نویسنده

گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه اردکان، اردکان، ایران

چکیده

در برخی از کاربردهای دنیای واقعی، داده‌هایی با ابعاد بالا وجود دارند که چالش‌های محاسباتی زیادی را ایجاد کرده‌اند. یکی از تکنیک‌های موثر برای کاهش ابعاد داده‌ها، انتخاب ویژگی است که با انتخاب زیرمجموعه مناسبی از ویژگی‌ها باعث سادگی مدل و بهبود کارایی آن می‌شود. در بسیاری از این کاربردها، برچسب زدن داده‌ها امری زمان‌بر و پرهزینه است که باعث می‌شود داده‌های برچسب‌دار کمی وجود داشته باشند و حجم عظیمی از داده‌های بدون برچسب در دسترس باشند. در چنین کاربردهایی، روش‌های انتخاب ویژگی نیمه‌نظارتی می‌توانند با استفاده از اطلاعات برچسب داده‌های برچسب‌دار و اطلاعات توزیع و ساختار هندسی داده‌های برچسب‌دار و بدون برچسب، فرایند انتخاب ویژگی را انجام دهند. در اکثر روش‌های انتخاب ویژگی نیمه‌نظارتی، با ایجاد یک گراف همسایگی، ویژگی‌های مناسب از طریق بررسی توانایی‌ آن‌ها در حفظ ساختار هندسی گراف ارزیابی می‌شوند. در روش‌های کلاسیک انتخاب ویژگی نیمه‌نظارتی مبتنی بر گراف، ویژگی‌ها به صورت جداگانه ارزیابی می‌شوند و همبستگی بین ویژگی‌ها در هنگام انتخاب ویژگی در نظر گرفته نمی‌شود. روش‌های انتخاب ویژگی تُنک با در نظر گرفتن همبستگی بین ویژگی‌ها، ماتریس انتقال بهینه تُنک برای انتخاب ویژگی‌ را محاسبه می‌نمایند. در این مقاله با بررسی روش‌های یادگیری نیمه‌نظارتی، مروری بر روش‌های انتخاب ویژگی نیمه‌نظارتی تُنک مبتنی بر گراف انجام می‌شود که با استفاده از عبارت تنظیم مبتنی بر مدل‌های تُنک و با ایجاد گراف همسایگی، ویژگی‌های مناسب را انتخاب می‌کنند. این روش‌ها ضمن برطرف کردن مشکل روش‌های انتخاب ویژگی کلاسیک، با ایجاد یک گراف همسایگی از داده‌ها ماتریس انتقال بهینه تُنک برای انتخاب ویژگی را محاسبه می‌نمایند.

کلیدواژه‌ها

عنوان مقاله [English]

A review on graph-based semi-supervised sparse feature selection methods

نویسنده [English]

  • Razieh Sheikhpour
Department of Computer Engineering, Faculty of Engineering, Ardakan University, P.O. Box 184, Ardakan, Iran
چکیده [English]

In some real-world applications, there is high-dimensional data which has led to many computational challenges. Feature selection is an effective technique for data dimensionality reduction, which simplifies the model and improves its performance by selecting the appropriate subset of features. In many of these applications, labeling of data is costly and time consuming, leaving little labeled data available and large amounts of unlabeled data available. In such applications, semi-supervised feature selection methods perform the feature selection process using the information of labeled data, and the distribution and geometric structure of labeled and unlabeled data. In most semi-supervised feature selection methods, a neighborhood graph is created and the importance of features is evaluated via their ability to maintain the geometric structure of the graph. In classical graph-based semi-supervised feature selection methods, the features are evaluated one by one and the correlation between features is not considered in feature selection process. To overcome this problem, sparse feature selection methods have been presented which consider the correlation between features, and calculate the optimal sparse transformation matrix for feature selection. In this paper, we investigate the semi-supervised learning methods, and review the graph-based semi- supervised sparse feature selection methods which select the appropriate features using the graph created by the labeled and unlabeled data, and the sparse regularization term. These methods solve the problem of classical semi-supervised methods by considering the correlation between features, create a neighborhood graph using the labeled and unlabeled data, calculate the graph Laplacian matrix, and compute the optimal sparse transformation matrix for feature selection.

کلیدواژه‌ها [English]

  • Semi-supervised feature selection
  • Semi-supervised learning
  • Sparse models
  • Graph Laplacian

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پژوهش های نظری و کاربردی هوش ماشینی
دوره 1، شماره 1
فروردین 1402
صفحه 73-87

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