MULTI-STAGE OMP sparse coding using local matching pursuit atoms selection

2013 ◽  
Author(s):  
Mahdi Marsousi ◽  
Kaveh Abhari ◽  
Paul Babyn ◽  
Javad Alirezaie
Keyword(s):  
Sparse Coding ◽  
Matching Pursuit ◽  
Multi Stage

scholarly journals YASS: Yet Another Spike Sorter

2017 ◽  
Author(s):  
JinHyung Lee ◽  
David Carlson ◽  
Hooshmand Shokri ◽  
Weichi Yao ◽  
Georges Goetz ◽  
...  
Keyword(s):  
Dirichlet Process ◽  
Large Scale ◽  
Matching Pursuit ◽  
Spike Sorting ◽  
Dirichlet Process Mixture ◽  
Electrode Arrays ◽  
Neural Signals ◽  
Model Framework ◽  
Multi Stage ◽  
Computational Bottleneck

AbstractSpike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data. This manuscript describes an efficient, reliable pipeline for spike sorting on dense multi-electrode arrays (MEAs), where neural signals appear across many electrodes and spike sorting currently represents a major computational bottleneck. We present several new techniques that make dense MEA spike sorting more robust and scalable. Our pipeline is based on an efficient multi-stage “triage-then-cluster-then-pursuit” approach that initially extracts only clean, high-quality waveforms from the electrophysiological time series by temporarily skipping noisy or “collided” events (representing two neurons firing synchronously). This is accomplished by developing a neural network detection method followed by efficient outlier triaging. The clean waveforms are then used to infer the set of neural spike waveform templates through nonparametric Bayesian clustering. Our clustering approach adapts a “coreset” approach for data reduction and uses efficient inference methods in a Dirichlet process mixture model framework to dramatically improve the scalability and reliability of the entire pipeline. The “triaged” waveforms are then finally recovered with matching-pursuit deconvolution techniques. The proposed methods improve on the state-of-the-art in terms of accuracy and stability on both real and biophysically-realistic simulated MEA data. Furthermore, the proposed pipeline is efficient, learning templates and clustering much faster than real-time for a ≃ 500-electrode dataset, using primarily a single CPU core.

scholarly journals Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

2019 ◽  
Vol 5 (11) ◽  
pp. 85 ◽  
Author(s):  
Ayan Chatterjee ◽  
Peter W. T. Yuen
Keyword(s):  
Dictionary Learning ◽  
Sparse Coding ◽  
Clustering Algorithm ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Scene Reconstruction ◽  
Data Sets ◽  
Practical Applications ◽  
Input Dataset ◽  
Better Than

This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary D u . The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim package.

scholarly journals Variable Length K-SVD: A New Dictionary Learning Approach and Multi-Stage OMP Method for Sparse Representation

2021 ◽  
Author(s):  
Mahdi Marsousi
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Input Data ◽  
Matching Pursuit ◽  
Computational Cost ◽  
Research Field ◽  
Learning Approach ◽  
Data Set ◽  
Multi Stage ◽  
Blocking Artifact

The Sparse representation research field and applications have been rapidly growing during the past 15 years. The use of overcomplete dictionaries in sparse representation has gathered extensive attraction. Sparse representation was followed by the concept of adapting dictionaries to the input data (dictionary learning). The K-SVD is a well-known dictionary learning approach and is widely used in different applications. In this thesis, a novel enhancement to the K-SVD algorithm is proposed which creates a learnt dictionary with a specific number of atoms adapted for the input data set. To increase the efficiency of the orthogonal matching pursuit (OMP) method, a new sparse representation method is proposed which applies a multi-stage strategy to reduce computational cost. A new phase included DCT (PI-DCT) dictionary is also proposed which significantly reduces the blocking artifact problem of using the conventional DCT. The accuracy and efficiency of the proposed methods are then compared with recent approaches that demonstrate the promising performance of the methods proposed in this thesis.

scholarly journals БАГАТОШАРОВА МОДЕЛЬ ТА МЕТОД НАВЧАННЯ ДЛЯ ДЕТЕКТУВАННЯ ШКІДЛИВОГО ТРАФІКУ НА ОСНОВІ АНСАМБЛЮ ДЕРЕВ РІШЕНЬ

2020 ◽  
pp. 92-101
Author(s):  
В’ячеслав Васильович Москаленко ◽  
Микола Олександрович Зарецький ◽  
Альона Сергіївна Москаленко ◽  
Антон Михайлович Кудрявцев ◽  
Віктор Анатолійович Семашко
Keyword(s):  
Sparse Coding ◽  
Matching Pursuit ◽  
Decision Rules ◽  
Training Data ◽  
Maximum Efficiency ◽  
Explanatory Factors ◽  
Feature Extractor ◽  
Knowledge Distillation ◽  
Traffic Detector ◽  
Regularized Method

The model and training method of multilayer feature extractor and decision rules for a malware traffic detector is proposed. The feature extractor model is based on a convolutional sparse coding network whose sparse encoder is approximated by a regression random forest model according to the principles of knowledge distillation. In this case, an algorithm of growing sparse coding neural gas has been developed for unsupervised training the features extractor with automatic determination of the required number of features on each layer. As for feature extractor, at the training phase to implement of sparse coding the greedy L1-regularized method of Orthogonal Matching Pursuit was used, and at the knowledge distillation phase, the L1-regularized method at the least angles (Least regression algorithm) was additionally used. Due to the explaining-away effect, the extracted features are uncorrelated and robust to noise and adversarial attacks. The proposed feature extractor is unsupervised trained to separate the explanatory factors and allows to use the unlabeled training data, which are usually quite large, with the maximum efficiency. As a model of the decision rules proposed to use the binary encoder of input observations based on an ensemble of decision trees and information-extreme closed hyper-surfaces (containers) for class separation, that are recovery in radial-basis of Hemming' binary space. The addition of coding trees is based on the boosting principle, and the radius of class containers is optimized by direct search. The information-extreme classifier is characterized by low computational complexity and high generalization capacity for small sets of labeled training data. The verification results of the trained model on open CTU test data sets confirm the suitability of the proposed algorithms for practical application since the accuracy of malware traffic detection is 96.1 %.

scholarly journals Variable Length K-SVD: A New Dictionary Learning Approach and Multi-Stage OMP Method for Sparse Representation

2021 ◽  
Author(s):  
Mahdi Marsousi
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Input Data ◽  
Matching Pursuit ◽  
Computational Cost ◽  
Research Field ◽  
Learning Approach ◽  
Data Set ◽  
Multi Stage ◽  
Blocking Artifact

The Sparse representation research field and applications have been rapidly growing during the past 15 years. The use of overcomplete dictionaries in sparse representation has gathered extensive attraction. Sparse representation was followed by the concept of adapting dictionaries to the input data (dictionary learning). The K-SVD is a well-known dictionary learning approach and is widely used in different applications. In this thesis, a novel enhancement to the K-SVD algorithm is proposed which creates a learnt dictionary with a specific number of atoms adapted for the input data set. To increase the efficiency of the orthogonal matching pursuit (OMP) method, a new sparse representation method is proposed which applies a multi-stage strategy to reduce computational cost. A new phase included DCT (PI-DCT) dictionary is also proposed which significantly reduces the blocking artifact problem of using the conventional DCT. The accuracy and efficiency of the proposed methods are then compared with recent approaches that demonstrate the promising performance of the methods proposed in this thesis.

scholarly journals A Study Of Three Dicitonary Learning Algorithms

2021 ◽  
Author(s):  
Zhiliang Xing
Keyword(s):  
Sparse Representation ◽  
Sparse Coding ◽  
Execution Time ◽  
Matching Pursuit ◽  
Data Extraction ◽  
Learning Algorithms ◽  
Global Optimum ◽  
Top Down ◽  
New Techniques ◽  
Efficiency Performance

Sparse Representation is a topic that has been gaining popularity in recent years due to its efficiency, performance and its applications in communication and data extraction fields. A number of algorithms exist that can be used to implement sparse coding techniques in different fields which include K-SVD, ODL, OMP etc. In this project one of the most popular sparse algorithms, the OMP (Orthogonal Matching Pursuit) technique, is investigatedin depth. Since OMP is not capable of finding the global optimum, a Top-Down Search (TDS) algorithm is proposed in this project to achieve much better results by sacrificing the execution time. Another contribution of this project is to investigate the properties of dictionary by modifying the frequency and shifting the phase of a standard Discrete Cosine Transfer (DCT) dictionary. The results of this project show that the performance of sparse coding algorithm still has room for improvement using new techniques.

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