نتایج جستجو برای: الگوریتم k svd
تعداد نتایج: 403198 فیلتر نتایج به سال:
In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, feature extraction, and more. Recent activit...
Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during detection process. This results in a large amount noise return signal. To reduce extract useful signal, novel denoising method combined with variational modal decomposition (VMD), sparrow search algorithm (SSA) singular value (SVD) proposed. The SSA used optimize numbe...
OBJECTIVE To compare the characteristics and outcomes of patients with atrial fibrillation (AF) and aortic stenosis (AS) with patients with AF with mitral regurgitation (MR) or aortic regurgitation (AR) and patients without significant valve disease (no SVD). METHODS Using Rivaroxaban Once-Daily, Oral, Direct Factor Xa Inhibition Compared With Vitamin K Antagonism for Prevention of Stroke and...
This article presents a multiclassifier approach for multiclass/multilabel document categorization problems. For the categorization process, we use a reduced vector representation obtained by SVD for training and testing documents, and a set of k-NN classifiers to predict the category of test documents; each k-NN classifier uses a reduced database subsampled from the original training database....
Latent Semantic Analysis (LSA) is one of the main variants of vector space methods for information retrieval, and continues to be an active area of research, both in the theory of how LSA works and in the practical applications of the method. Alternatives to the Singular Value Decomposition (SVD) have been explored that o er improvements in storage, speed, updating or other advantages, especial...
Recommender system is widely implemented in various fields. Collaborative Filtering one of the most used recommender paradigms because it easy to use. K-means clustering algorithm use Filtering. This can predict item rating that will be given by a user. Rating predicted calculating average item. The performance this low selects initial centroid randomly. causes high errors prediction. To obtain...
عموماً حضور نوفه در تحقیقات و اندازه‌گیری‌های ژئوفیزیکی امری اجتناب‌ناپذیر است و بسته به نوع و میزان آن، نتایج به‌دست آمده تحت تاثیر قرار می‌گیرند. از این‌رو مسئله تفکیک نوفه از سیگنال، بخشی مهم در پردازش داده‌های ژئوفیزیکی است. از طرف دیگر، محققان در ژئوفیزیک به دنبال به‌دست آوردن مشخصات فیزیکی درون زمین با استفاده از اندازه‌گیری (داده‌های) غیر مستقیم هستند که در سطح یا نزدیک به سطح زمین صورت م...
خوشه‌بندی یکی از روش‌های پرکاربرد در بسیاری از زمینه‌های علمی است که در آن تلاش می‌شود داده‌ها داخل گروه‌ها براساس درجه‌ی شباهت قرار گیرند. الگوریتم‌های ابتکاری و فراابتکاری زیادی برای حل مسئله‌ی خوشه‌بندی ارائه شده است. یکی از روش‌های ابتکاری پرکاربرد، K-means است. این روش، به‌دلیل وابستگی به حالت اولیه، معمولاً به بهینه‌یمحلی همگرا می‌شود. در این مقاله به‌منظور فرار از بهینه‌ی محلی، الگوری...
MOTIVATION Multidimensional scaling (MDS) is a well-known multivariate statistical analysis method used for dimensionality reduction and visualization of similarities and dissimilarities in multidimensional data. The advantage of MDS with respect to singular value decomposition (SVD) based methods such as principal component analysis is its superior fidelity in representing the distance between...
Graphs have so many applications in real world problems. When we deal with huge volume of data, analyzing data is difficult or sometimes impossible. In big data problems, clustering data is a useful tool for data analysis. Singular value decomposition(SVD) is one of the best algorithms for clustering graph but we do not have any choice to select the number of clusters and the number of members ...
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