[PDF] Top 20 A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization
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A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization
... several non-trivially different pairs of A, W may reconstruct the data X equally ...This non-identifiability of the NMF solution space has been studied in detail in the theoretical literature (Pan and ... See full document
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A Non negative Matrix Tri factorization Approach to Sentiment Classification with Lexical Prior Knowledge
... are based on a constrained non- negative tri-factorization of the term-document matrix, which can be implemented using simple update ...term-document matrix, in what may be ... See full document
9
Global Analytic Solution of Fully-observed Variational Bayesian Matrix Factorization
... the non-convexity of VB methods has been one of the important challenges in the Bayesian machine learning community, since it sometimes prevented us from applying the VB methods to highly complex real-world ... See full document
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A Non negative Matrix Factorization Based Approach for Active Dual Supervision from Document and Word Labels
... document-term matrix and to perform dual supervision for semi-supervised sentiment ...is based on the dual supervision framework using constrained non-negative ... See full document
10
Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy
... NMF based on maximizing correntropy criterion ...Another approach is preserving the geometric struc- ture of data based on the manifold assumption of data ...W based on l 2 distance (or KL ... See full document
10
Voice-based Age and Gender Recognition using Training Generative Sparse Model
... sparse non-negative matrix factorization (SNMF)with the incoherency concepts in order to represent the male and female voice components and recognize the gender/age of ...these ... See full document
7
NDMSCS: A Topic Based Chinese Microblog Polarity Classification System
... use non- negative matrix factorization to find the topic rel- evant ...Our approach includes an ex- tensive usage of Python based NLP and machine learning resources for ... See full document
5
Multimodal voice conversion based on non-negative matrix factorization
... VC approach is exemplar-based, which differs from conventional GMM-based ...NMF approach is advantageous in that it results in a more natural-sounding converted voice compared to con- ... See full document
9
Iterative Weighted Non-smooth Non-negative Matrix Factorization for Face Recognition
... Face recognition has been considered as one of the most challenging problems in computer vision and image processing communities since two decades ago. A well- established and widely used method in face recognition is ... See full document
10
Testing supervised classifiers based on non-negative matrix factorization to musical instrument classification
... Non-negative matrix factorization (NMF) is a subspace method for basis decomposition ...data matrix containing the training vec- tors of all the available ...classification ... See full document
6
Applying supervised classifiers based on non-negative matrix factorization to musical instrument classification
... new approach for automatic audio classification us- ing non-negative matrix factorization (NMF) is ...proposed approach were performed for musical instrument ... See full document
5
Theoretical Analysis of Bayesian Matrix Factorization
... Recently, variational Bayesian (VB) techniques have been applied to probabilistic matrix factor- ization and shown to perform very well in ...VB matrix factorization (VBMF) ...by ... See full document
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Posteriori Regularization based Non Negative Matrix Factorization approach for Speech Enhancement
... Speech enhancement improves quality and intelligibility. For robust noise reduction and analysis of non-Stationary signal analysis NMF technique is used. From the experiment results it shows that NR-NMF provides ... See full document
6
Feature enhancement of reverberant speech by distribution matching and non-negative matrix factorization
... Previous studies have attempted to counteract the con- volutional distortion caused by reverberation using a number of denoising methods, such as frequency domain linear prediction [3], modulation filtered spectrograms ... See full document
14
Intelligent Intrusion Detection in Computer Networks using Swarm Intelligence
... If ε is above a threshold, the block of data associated with the data vector t is then considered as normal. Otherwise, it is treated as anomalous with an anomaly index ε. After all the anomaly index of per process has ... See full document
9
Robust Non Blind Colour Image Watermarking Using Non Negative Matrix Factorization Technique in DWT Domain
... In spatial domain, watermarks are embedded directly in the image pixels. This has the advantages of easy implementation and low complexity when compare to frequency domain. However, the major issue in the spatial domain ... See full document
10
Bayesian Deep Collaborative Matrix Factorization
... items. Matrix factorization (MF) (Mnih and Salakhut- dinov 2008) is one of the most commonly used CF methods due to its effectiveness and ...feedback matrix. However, traditional MF methods suffer ... See full document
8
Area Correlated Spectral Unmixing Based on Bayesian Nonnegative Matrix Factorization
... But in the case of comprehensive analysis, we need also consider some other information besides the hyperspectral image itself, such as spectra from the spectral library and hyperspectral images observed at different ... See full document
6
Non Negative Matrix Factorization Based UKF Algorithm for Constant Modulus Signals in Adaptive Beamforming
... We see there is a better improvement of sensor array gain, signal to interference plus noise ratio SINR and mean squared deviation MSD as the noise variance and the array size increase w[r] ... See full document
10
Piano multipitch estimation using sparse coding embedded deep learning
... As the foundation of many applications, multipitch estimation problem has always been the focus of acoustic music processing; however, existing algorithms perform deficiently due to its complexity. In this paper, we ... See full document
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