Denoising Autoencoder-Based Missing Value Imputation for Smart Meters
نویسندگان
چکیده
منابع مشابه
Missing Value Imputation Based on Data Clustering
We propose an efficient nonparametric missing value imputation method based on clustering, called CMI (Clustering-based Missing value Imputation), for dealing with missing values in target attributes. In our approach, we impute the missing values of an instance A with plausible values that are generated from the data in the instances which do not contain missing values and are most similar to t...
متن کاملMachine Learning Based Missing Value Imputation Method for Clinical Dataset
Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore t...
متن کاملPerformance Evaluation of L1-norm-based Microarray Missing Value Imputation
l1-norm minimization was utilized in the imputation of microarray missing values, which is an important procedure in bioinformatics experiments. Two l1 approaches, based on the framework of local least squares (LLS) and iterative biclusterbased least squares (bicluster-iLLS) respectively, were employed. Imputed datasets of the l1 approaches were compared with those of traditional l2 methods. Th...
متن کاملReverberant speech recognition based on denoising autoencoder
Denoising autoencoder is applied to reverberant speech recognition as a noise robust front-end to reconstruct clean speech spectrum from noisy input. In order to capture context effects of speech sounds, a window of multiple short-windowed spectral frames are concatenated to form a single input vector. Additionally, a combination of short and long-term spectra is investigated to properly handle...
متن کاملSpeech enhancement based on deep denoising autoencoder
We previously have applied deep autoencoder (DAE) for noise reduction and speech enhancement. However, the DAE was trained using only clean speech. In this study, by using noisyclean training pairs, we further introduce a denoising process in learning the DAE. In training the DAE, we still adopt greedy layer-wised pretraining plus fine tuning strategy. In pretraining, each layer is trained as a...
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: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2976500