Diễn giải không gian đa quan điểm được nâng cao từ điều chỉnh mặt phẳng và ràng buộc tensor định mức thấp

Guoqing Liu1,2, Hongwei Ge1,2, Ting Li1,2, Shuzhi Su3, Shuangxi Wang1,2
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
2Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education, Wuxi, China
3School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, China

Tóm tắt

Trong bài báo này, để trích xuất thông tin đa dạng từ dữ liệu đa quan điểm và cải thiện hiệu suất phân cụm của phương pháp học đa quan điểm, chúng tôi giới thiệu phương pháp diễn giải không gian đa quan điểm được nâng cao từ điều chỉnh mặt phẳng và ràng buộc tensor định mức thấp (MSERMLRT). Mô hình của chúng tôi sử dụng một tensor để khám phá mối tương quan giữa các quan điểm. Tensor bị ràng buộc với định mức thấp, và mục đích của quá trình này là giảm thông tin thừa của diễn giải không gian được học. Mô hình này cũng sử dụng thông tin mặt phẳng từ dữ liệu đa quan điểm và áp đặt một ràng buộc thưa trên tích của nó với ma trận diễn giải không gian có chuyển vị để nâng cao cấu trúc khối chéo của diễn giải không gian, từ đó cải thiện hiệu quả phân cụm của nó ở một mức độ nhất định. Chúng tôi cũng thiết kế một phương pháp hữu ích để giải quyết mô hình MSERMLRT và phân tích sự hội tụ của phương pháp của chúng tôi cả về lý thuyết và thực nghiệm. Hiệu suất phân cụm trên một số tập dữ liệu khó khăn cho thấy mô hình MSERMLRT vượt trội hơn nhiều phương pháp phân cụm đa quan điểm tiên tiến khác.

Từ khóa

#không gian đa quan điểm #điều chỉnh mặt phẳng #ràng buộc tensor định mức thấp #phân cụm #hội tụ

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