ارائه روشی جدید بر مبنای تجزیه ماتریس غیر منفی برای کاهش ابعاد
محورهای موضوعی : مهندسی برق و کامپیوتر
مهدی حسین زاده اقدم
1
,
مرتضی آنالویی
2
,
جعفر تنها
3
1 - دانشگاه بناب،دانشکده فنی و مهندسی
2 - دانشگاه علم و صنعت ایران،دانشكده مهندسي كامپيوتر
3 - دانشگاه تبریز،دانشكده مهندسي برق و كامپيوتر
کلید واژه: کاهش ابعاد, تجزیه ماتریسی غیر منفی, نرم فروبنیوس, قوانین به روز رسانی, خوشه‌بندی متن,
چکیده مقاله :
یادگیری ماشین در طی دهه‌های گذشته به دلیل طیف گسترده کاربردهای آن مورد استفاده زیادی قرار گرفته است. در اکثر کاربردهای یادگیری ماشین مانند خوشه‌بندی و طبقهبندی، ابعاد داده‌ها زیاد می‌باشد و استفاده از روش‌های کاهش ابعاد داده ضروری است. تجزیه ماتریس غیر منفی با استفاده از استخراج ویژگی‌ها معنایی از داده‌های با ابعاد زیاد کاهش ابعاد را انجام می‌دهد و در تجزیه ماتریس غیر منفی فقط نحوه مدل‌سازی هر بردار ویژگی در ماتریس‌های تجزیه‌شده را در نظر می‌گیرد و روابط بین بردارهای ویژگی را نادیده می‌گیرد. ارتباطات میان بردارهای ویژگی، تجزیه بهتری را برای کاربردهای یادگیری ماشین فراهم می‌کنند. در این مقاله، یک روش بر مبنای تجزیه ماتریس غیر منفی برای کاهش ابعاد داده‌ها ارائه شده که محدودیت‌هایی را بر روی هر جفت‌بردارهای ویژگی با استفاده از معیارهای مبتنی بر فاصله ایجاد می‌کند. روش پیشنهادی از نرم فروبنیوس به عنوان تابع هزینه برای ایجاد قوانین به روز رسانی استفاده می‌کند. نتایج آزمایش‌ها روی مجموعه داده‌ها نشان می‌دهد که قوانین به روز رسانی ضربی ارائه‌شده، سریع همگرا می‌شوند و در مقایسه با الگوریتم‌های دیگر نتایج بهتری را ارائه می‌کنند.
Machine learning has been widely used over the past decades due to its wide range of applications. In most machine learning applications such as clustering and classification, data dimensions are large and the use of data reduction methods is essential. Non-negative matrix factorization reduces data dimensions by extracting latent features from large dimensional data. Non-negative matrix factorization only considers how to model each feature vector in the decomposed matrices and ignores the relationships between feature vectors. The relationships between feature vectors provide better factorization for machine learning applications. In this paper, a new method based on non-negative matrix factorization is proposed to reduce the dimensions of the data, which sets constraints on each feature vector pair using distance-based criteria. The proposed method uses the Frobenius norm as a cost function to create update rules. The results of experiments on the data sets show that the proposed multiplicative update rules converge rapidly and give better results than other algorithms.
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