Sparse representation based classification
Transform-domain sparse representation based classification for machinery vibration signals
... novel classification method for vibration signals, named Transform Domain Sparse Representation-based Classification (TDSRC), is ...high classification accuracy by three ...the ...
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Joint sparse representation based classification of rub-impact in rotating machinery with multiple acoustic emission sensors
... the classification of rub-impact in rotating machinery due to its high sensitivity, wide frequency response range and dynamic detection ...traditional classification method tailored to a single AE ...a ...
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Progressive sparse representation-based classification using local discrete cosine transform evaluation for image recognition
... progressive sparse representation-based classification algorithm using local discrete cosine transform (DCT) evaluation to perform face ...
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Latent dictionary learning for sparse representation based classification
... for sparse coding has shown promising results in classification tasks, while how to adaptively build the relationship between dictionary atoms and class labels is still an important open ...the ...
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Sparse Representation Based Classification for Face Recognition by k-LiMapS Algorithm
... 3 Face Recognition via k-LiMapS Here we show that k-LiMapS can be used in a FRS, enforcing the belief that sparsest representation is naturally discriminative. Our algorithm achieves small classification error by ...
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A COMPARATIVE STUDY OF LRC, NNLS, KNN, AND SPARSE REPRESENTATION BASED CLASSIFICATION METHODS BASED ON AUDIO FEATURES
... are based on a preliminary Fast Fourier Transformation (FFT) to obtain a representation of the audio signal in the frequency ...is based on the Mel frequency scale, which is linear at low frequencies ...
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Sparse representation based hyperspectral image compression and classification
... Time is in seconds using MATLAB 2014b implementation; Bit Rate: in bpppb. 5.5 Conclusion Sparse representation models a set of data based on dictionary and sparse coding. The dictionary is ...
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Metasample Based Robust Sparse Representation for Tumor Classification
... DNA microarray is a biotechnology that simultane- ously monitors the expression of tens of thousands genes in cells. There are many methods have been used in tu- mor classification through microarray gene ...
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Classification of machinery vibration signals based on group sparse representation
... Accurate classification of these vibration signals is helpful for the machinery fault ...regularization based sparse representation for classification (SRC) has obtained huge success in ...
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Classification of Medical Data Based On Sparse Representation Using Dictionary Learning
... the United States healthcare industry $316.4 billion. A large number of admissions to hospitals are unnecessary and avoidable. Due to inadequate preventive measures, CVD related fatalities continue to rise. It is ...
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A Method of Polarimetric Synthetic Aperture Radar Image Classification Based on Sparse Representation
... Sparse representation-based techniques have shown great potential for pattern recognition ...the sparse characteristics of the features for PolSAR image classification, a supervised ...
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A robust sparse representation model for hyperspectral image classification
... years, sparse representation classification (SRC) [ 19 ] emerged as another effective classification approach, which became widely adopted for HSI [ 20 – 34 ...joint sparse ...
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Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images
... better classification accuracy than the SRC, it has several drawbacks: first, with the fixed (size and shape) window strategy, many unrelated pixels may be chosen to the test pixel whilst correlated pixels may be ...
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Robust Visual Tracking and Vehicle Classification via Sparse Representation
... 5 S IMULTANEOUS T RACKING AND R ECOGNI - TION In this section, we extend our ` 1 tracker and propose a simulta- neous tracking and recognition method using ` 1 minimization in a probabilistic framework. When we locate ...
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Supervised classification via constrained subspace and tensor sparse representation
... via sparse representa- tion, has rapidly gained popularity in recent years and can be adapted to a wide range of applications based on the sparse solution of a linear ...tensor sparse ...
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Supervised classification via constrained subspace and tensor sparse representation
... via sparse representa- tion, has rapidly gained popularity in recent years and can be adapted to a wide range of applications based on the sparse solution of a linear ...tensor sparse ...
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Face Recognition on Smartphones via Optimised Sparse Representation Classification
... For this evaluation, we begin with an initial training set containing 16 classes. We use tabu search to obtain an opti- mised projection matrix based on the initial dictionary. We then add training classes to the ...
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Group sparse representation based on nonlocal spatial and local spectral similarity for hyperspectral imagery classification
... overall classification accuracies obtained by different methods tested, as a function of the number of labeled samples used for training and dictionary ...overall classification accuracies towards SVM with ...
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Dynamic Dictionary Optimization for Sparse-representation-based Face Classification using Local Difference Images
... new sparse-representation-based face-classification algo- rithm that exploits dynamic dictionary optimization on an extended dictionary using synthesized ...the classification accuracy, ...
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A Novel Inpainting Algorithm based on Sparse Representation
... Figure 1: Proposed dictionary Pruning algorithm In the proposed inpainting algorithm, texture distribution analysis is used to separate the missing area into homogenous and non homogenous textures and separate methods ...
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