Object Recognition Using Non-Negative Matrix Factorization with Sparseness Constraint and Neural Network
نویسندگان
چکیده
منابع مشابه
Non-negative Matrix Factorization with Sparseness Constraints
Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how explicitly incorporating the notion of ‘sparseness’ improves the found decompositions. Additionally, ...
متن کاملConvolutive Non-negative Matrix Factorisation with Sparseness Constraint
Discovering a parsimonious representation that reflects the structure of audio is a requirement of many machine learning and signal processing methods. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), which is a method for finding parts-based representations of non-negative data. We present an extension to NMF that is convolutive and forces a sparseness const...
متن کاملIterative Weighted Non-smooth Non-negative Matrix Factorization for Face Recognition
Non-negative Matrix Factorization (NMF) is a part-based image representation method. It comes from the intuitive idea that entire face image can be constructed by combining several parts. In this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “Iterative weighted non-smooth non-negative matrix factorization” (IWNS-NMF). A new cost fun...
متن کاملSpeech Denoising Using Non-negative Matrix Factorization with Kullback-Leibler Divergence and Sparseness Constraints
A speech denoising method based on Non-Negative Matrix Factorization (NMF) is presented in this paper. With respect to previous related works, this paper makes two contributions. First, our method does not assume a priori knowledge about the nature of the noise. Second, it combines the use of the Kullback-Leibler divergence with sparseness constraints on the activation matrix, improving the per...
متن کاملA Deep Non-Negative Matrix Factorization Neural Network
Recently, deep neural network algorithms have emerged as one of the most successful machine learning strategies, obtaining state of the art results for speech recognition, computer vision, and classification of large data sets. Their success is due to advancement in computing power, availability of massive amounts of data and the development of new computational techniques. Some of the drawback...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information
سال: 2019
ISSN: 2078-2489
DOI: 10.3390/info10020037