scholarly journals Frequency-separated principal component analysis of cortical population activity

2020 ◽  
Vol 124 (3) ◽  
pp. 668-681
Author(s):  
Jean-Philippe Thivierge
Keyword(s):  
Principal Component Analysis ◽  
Visual Cortex ◽  
Sensory Input ◽  
Low Frequency ◽  
Principal Component ◽  
Component Analysis ◽  
Population Activity ◽  
Standard Principal Component Analysis ◽  
Specific Frequency ◽  
Low Dimensional

A method termed frequency-separated principal component analysis (FS-PCA) is introduced for analyzing populations of simultaneously recorded neurons. This framework extends standard principal component analysis by extracting components of activity delimited to specific frequency bands. FS-PCA revealed that circuits of the primary visual cortex generate a broad range of components dominated by low-frequency activity. Furthermore, low-dimensional fluctuations in population activity modulated the response of individual neurons to sensory input.

scholarly journals Demixed principal component analysis of neural population data

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Dmitry Kobak ◽  
Wieland Brendel ◽  
Christos Constantinidis ◽  
Claudia E Feierstein ◽  
Adam Kepecs ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Population Data ◽  
Component Analysis ◽  
Neural Representation ◽  
Neural Population ◽  
Population Activity ◽  
Cortical Areas ◽  
Dimensionality Reduction Technique ◽  
Single Figure

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

Object Tracking Using Probabilistic Principal Component Analysis Based on Particle Filtering Framework

2011 ◽  
Vol 341-342 ◽  
pp. 790-797 ◽  
Author(s):  
Zhi Yan Xiang ◽  
Tie Yong Cao ◽  
Peng Zhang ◽  
Tao Zhu ◽  
Jing Feng Pan
Keyword(s):  
Principal Component Analysis ◽  
Object Tracking ◽  
Particle Filtering ◽  
Principal Component ◽  
Component Analysis ◽  
Dimensional Subspace ◽  
Target Object ◽  
Scale Invariant ◽  
Low Dimensional ◽  
Probabilistic Principal Component Analysis

In this paper, an object tracking approach is introduced for color video sequences. The approach presents the integration of color distributions and probabilistic principal component analysis (PPCA) into particle filtering framework. Color distributions are robust to partial occlusion, are rotation and scale invariant and are calculated efficiently. Principal Component Analysis (PCA) is used to update the eigenbasis and the mean, which can reflect the appearance changes of the tracked object. And a low dimensional subspace representation of PPCA efficiently adapts to these changes of appearance of the target object. At the same time, a forgetting factor is incorporated into the updating process, which can be used to economize on processing time and enhance the efficiency of object tracking. Computer simulation experiments demonstrate the effectiveness and the robustness of the proposed tracking algorithm when the target object undergoes pose and scale changes, defilade and complex background.

Laser Active Image-Denoising Based on Principal Component Analysis with Local Pixel Grouping

2014 ◽  
Vol 571-572 ◽  
pp. 753-756
Author(s):  
Wei Li Li ◽  
Xiao Qing Yin ◽  
Bin Wang ◽  
Mao Jun Zhang ◽  
Ke Tan
Keyword(s):  
Principal Component Analysis ◽  
Image Denoising ◽  
Principal Component ◽  
Speckle Noise ◽  
Component Analysis ◽  
Median Filtering ◽  
Denoising Method ◽  
Structure Information ◽  
Pixel Grouping ◽  
Low Dimensional

Denoising is an important issue for laser active image. This paper attempted to process laser active image in the low-dimensional sub-space. We adopted the principal component analysis with local pixel grouping (LPG-PCA) denoising method proposed by Zhang [1], and compared it with the conventional denoising method for laser active image, such as wavelet filtering, wavelet soft threshold filtering and median filtering. Experimental results show that the image denoised by LPG-PCA has higher BIQI value than other images, most of the speckle noise can be reduced and the detail structure information is well preserved. The low-dimensional sub-space idea is a new direction for laser active image denoising.

scholarly journals Dimensionality Reduction with Sparse Locality for Principal Component Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Pei Heng Li ◽  
Taeho Lee ◽  
Hee Yong Youn
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Sparse Representation ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Reconstruction Error ◽  
Dimensional Representation ◽  
Weighted Matrix ◽  
Low Dimensional

Various dimensionality reduction (DR) schemes have been developed for projecting high-dimensional data into low-dimensional representation. The existing schemes usually preserve either only the global structure or local structure of the original data, but not both. To resolve this issue, a scheme called sparse locality for principal component analysis (SLPCA) is proposed. In order to effectively consider the trade-off between the complexity and efficiency, a robust L2,p-norm-based principal component analysis (R2P-PCA) is introduced for global DR, while sparse representation-based locality preserving projection (SR-LPP) is used for local DR. Sparse representation is also employed to construct the weighted matrix of the samples. Being parameter-free, this allows the construction of an intrinsic graph more robust against the noise. In addition, simultaneous learning of projection matrix and sparse similarity matrix is possible. Experimental results demonstrate that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy and data reconstruction error.

scholarly journals Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs

Computation ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 78
Author(s):  
Shengkun Xie
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Power Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Eeg Signals ◽  
Feature Extraction Method ◽  
Functional Principal Component ◽  
Low Dimensional

Feature extraction plays an important role in machine learning for signal processing, particularly for low-dimensional data visualization and predictive analytics. Data from real-world complex systems are often high-dimensional, multi-scale, and non-stationary. Extracting key features of this type of data is challenging. This work proposes a novel approach to analyze Epileptic EEG signals using both wavelet power spectra and functional principal component analysis. We focus on how the feature extraction method can help improve the separation of signals in a low-dimensional feature subspace. By transforming EEG signals into wavelet power spectra, the functionality of signals is significantly enhanced. Furthermore, the power spectra transformation makes functional principal component analysis suitable for extracting key signal features. Therefore, we refer to this approach as a double feature extraction method since both wavelet transform and functional PCA are feature extractors. To demonstrate the applicability of the proposed method, we have tested it using a set of publicly available epileptic EEGs and patient-specific, multi-channel EEG signals, for both ictal signals and pre-ictal signals. The obtained results demonstrate that combining wavelet power spectra and functional principal component analysis is promising for feature extraction of epileptic EEGs. Therefore, they can be useful in computer-based medical systems for epilepsy diagnosis and epileptic seizure detection problems.

scholarly journals Performance Analysis of Dimensionality Reduction Techniques in the Context of Clustering

2019 ◽  
Vol 8 (S3) ◽  
pp. 66-71
Author(s):  
T. Sudha ◽  
P. Nagendra Kumar
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
High Dimensional Data ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Probabilistic Principal Component Analysis

Data mining is one of the major areas of research. Clustering is one of the main functionalities of datamining. High dimensionality is one of the main issues of clustering and Dimensionality reduction can be used as a solution to this problem. The present work makes a comparative study of dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis in the context of clustering. High dimensional data have been reduced to low dimensional data using dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis. Cluster analysis has been performed on the high dimensional data as well as the low dimensional data sets obtained through t-distributed stochastic neighbour embedding and Probabilistic principal component analysis with varying number of clusters. Mean squared error; time and space have been considered as parameters for comparison. The results obtained show that time taken to convert the high dimensional data into low dimensional data using probabilistic principal component analysis is higher than the time taken to convert the high dimensional data into low dimensional data using t-distributed stochastic neighbour embedding.The space required by the data set reduced through Probabilistic principal component analysis is less than the storage space required by the data set reduced through t-distributed stochastic neighbour embedding.

Addressing the Sparsity of Laser-Induced Breakdown Spectroscopy Data with Randomized Sparse Principal Component Analysis

2021 ◽  
Author(s):  
Erik Kepes ◽  
Jakub Vrabel ◽  
Pavel Pořízka ◽  
Jozef Kaiser
Keyword(s):  
Principal Component Analysis ◽  
Emission Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensionality ◽  
Laser Induced Breakdown Spectroscopy ◽  
Sparse Principal Component Analysis ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Low Dimensional

Emission spectra yielded by laser-induced breakdown spectroscopy (LIBS) exhibit high dimensionality, redundancy, and sparsity. The high dimensionality is often addressed by principal component analysis (PCA) which creates a low dimensional...

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