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Search results “Principal component analysis image recognition”
What is PCA (explained from face recognition point of view)
 
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Understand concept of 'Principal component Analysis' in the light of face recognition. http://fewtutorials.bravesites.com
Views: 31232 Mahvish Nasir
How PCA Recognizes Faces - Algorithm In Simple Steps (3_3)
 
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Tutorial Level 4b - part 2 Understand how Principal Component Analysis recognizes faces. - Algorithm In Simple Steps (3_3) http://fewtutorials.bravesites.com
Views: 94569 Mahvish Nasir
PCA 10: eigen-faces
 
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Full lecture: http://bit.ly/PCA-alg We can perform PCA on photographs of faces. First we unfold each bitmap into one big vector. We run PCA and find principal components (eigenvectors) which represent salient properties of faces. These eigenvectors can be folded back into a bitmap, which can be visualized and are called eigenfaces.
Views: 65611 Victor Lavrenko
Principal Component Analysis (PCA)
 
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This Lecture Describes Principal Component Analysis (PCA) with the help of an easy example.
Views: 114723 Saurabh Singh
Principal Components Analysis - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-649069103/m-661438544 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 291103 Udacity
Face Recognition||Principal component Analysis||PCA
 
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face recognition system by using Principal Component Analysis (PCA). PCA is a statistical approach used for reducing the number of variables in face recognition. In PCA, every image in the training set is represented as a linear combination of weighted eigenvectors called eigenfaces.
Views: 682 Yashwant
Principal Component Analysis (PCA) clearly explained (2015)
 
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NOTE: On April 2, 2018 I updated this video with a new video that goes, step-by-step, through PCA and how it is performed. Check it out! https://youtu.be/FgakZw6K1QQ RNA-seq results often contain a PCA or MDS plot. This StatQuest explains how these graphs are generated, how to interpret them, and how to determine if the plot is informative or not. I've got example code (in R) for how to do PCA and extract the most important information from it on the StatQuest website: https://statquest.org/2015/08/13/pca-clearly-explained/ For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest
Principal Component Analysis(PCA) Explained with Solved Example in Hindi ll Machine Learning Course
 
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Views: 22094 5 Minutes Engineering
Lecture: PCA for Face Recognition
 
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We demonstrate the power of the SVD/PCA framework on the computer vision problem of face recognition
Views: 26312 AMATH 301
Principal Component Analysis (PCA) in ArcGIS (GIS Tutorial)
 
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Tutorial about how to perform Principal Component Analysis or PCA to get the optimum spectral information from multispectral or hyperspectral satellite imagery, performed in ArcGIS version 10.6
Views: 3777 GEO 2004
Principal Component Analysis- Part I
 
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The theory of PCA for Face recognition using Matlab,
Views: 3640 rashi agrawal
PATTERN RECOGNITION PLOT INITIALIZATION AND PCA
 
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Pattern recognition plays a crucial part in the field of technology and can be used as a very general term. Find out about pattern recognition by diving into this series with us where we will explore pattern recognition as it relates to data science and machine learning used in the industry today. We will examine and use a few different models such as a K Nearest Neighbor algorithm and a Random Forest Classifier. In addition to our models, we will also utilize Sci-kit learn, the new trending library that allows us to build these incredible models with ease. Overall, the series will be a nice introduction to modern pattern recognition methodologies and approaches that will help get you started or expand upon your current skill set.
Views: 377 SuperDataScience
Final Year Projects | Principal Component Analysis for Hyperspectral Image Classification
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 841 Clickmyproject
Independent Components Analysis - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-649069103/m-661438547 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 30232 Udacity
Mod-05 Lec-34 Principal Components
 
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Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 2703 nptelhrd
PCA For Dimensionality Reduction in Pattern Recognition, a slecture by Khalid Tahboub
 
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This is a slecture for Prof. Boutin's course on Statistical Pattern Recognition (ECE662) made by Purdue ECE student Khalid Tahboub. The complete slecture is posted at https://www.projectrhea.org/rhea/index.php/Pca_khalid To view other slectures on the same topic go to the ECE662 course wiki at https://www.projectrhea.org/rhea/index.php/2014_Spring_ECE_662_Boutin For more information about slectures, go to http://slectures.projectrhea.org
Views: 9990 Project Rhea
Face Recognition using entropy weighted pca array under variation of lightning conditions
 
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-Face recognition Algorithm based on PCA -Face Recognition using Entropy Weighted Patch PCA Array under Variation of Lighting Conditions from a Single Sample Image per Person
Views: 107 superman
Principal Component Analysis(PCA) of Images in Python
 
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Principal Component Analysis of Images. First we've to convert the images into gray scale images. I got the code from a book Programming Computer Vision with Python by Jan Erik Solem, I've just added few lines to give path for the images and perform the PCA and saved the data for the further analysis. In the next video I will show you the face recognition with this. link: https://www.safaribooksonline.com/library/view/programming-computer-vision/9781449341916/ch01.html
Views: 9067 casual_coding
PCA for Facial Recognition - Intro to Machine Learning
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 10332 Udacity
Face Recognition using Principal Component Analysis - Part 2.2
 
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Digital image processing using Matlab You can find the database at the follwoing link: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html The database contains 400 pictures of 40 subjects.
Views: 642 Anamika Aggarwal
Principal Component Analysis (PCA) with Example | Machine Learning Tutorial
 
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Views: 4095 Muo sigma classes
Principal Component Analysis _ Digital Image Processing
 
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How to do principal component analysis on satellite imagery.
Views: 12391 Fahed Alhaj Mohamad
PCA, SVD
 
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Linear dimensionality reduction: principal components analysis (PCA) and the singular value decomposition (SVD)
Views: 67120 Alexander Ihler
Lecture 14.4 —  Dimensionality Reduction | Principal Component Analysis Algorithm — [ Andrew Ng ]
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Principal Component Analysis Tutorial Part 1 | Python Machine Learning Tutorial Part 3
 
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Principal Component Analysis Tutorial | Python Machine Learning Tutorial Part 3 https://acadgild.com/big-data/data-science-training-certification?aff_id=6003&source=youtube&account=CeXxokx8izc&campaign=youtube_channel&utm_source=youtube&utm_medium=python-machine-learning-pca-part3&utm_campaign=youtube_channel Machine learning algorithm typically finds the pattern and relationships in data without human intervention but the data that the machine learning algorithm had to deal with are usually very high dimensional. Welcome back to another session of Machine Learning Algorithms in Python tutorial powered by Acadgild. In the previous video, you have learned the linear regression. If you have missed the previous, please check the links as follows. Simple Linear Regression - https://www.youtube.com/watch?v=iL_iWFSzjK8&t=7s Implementing Linear Regression in Python - https://www.youtube.com/watch?v=M1mzE1IT-Is&t=225s In this machine learning tutorial, you will be able to learn Principal Component Analysis in python. Principal Component Analysis is a data pre-processing technique that allows the data to be transformed from higher dimensional space to a lower dimensional space in such a way that information that is crucial to drawing conclusions about the data is not lost. So, What Exactly is Principal Component Analysis (PCA)? • Principal Component Analysis (PCA) is a dimensionally-reduction technique that is often used to transform a high-dimensional dataset into smaller-dimensional subspace • PCA is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by some projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on. What are Principal Components? • Directions in which the data has the most variance – directions in which the data is most spread out • Mathematically, Eigenvectors of the symmetric covariance matrix of the original dataset • Each Eigenvector has the corresponding Eigenvalue. The Eigenvalue is a scalar that explains how much variance there is in the corresponding Eigenvector direction. Applications of Principal Component Analysis (PCA) • Compression • Visualization of high dimensional data • Speeding up of machine learning algorithms • Reducing noise from data Using Principal Component Analysis (PCA) for Compression: Once Eigenvectors are computed, compress the dataset by ordering k eigenvectors according to largest eigenvalues and compute Axk Reconstruct from the compressed version. We can reconstruct the data back by using inverse transformation mathematically represented by Axk x k.T Kindly, go through the complete video and please like, share and subscribe the channel. #PCA, #principalcomponentanalysis, #python, #datascience, #machinelearning Please like share and subscribe the channel for more such video. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 3020 ACADGILD
Principal Component Analysis (PCA) using Python (Scikit-learn)
 
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Principal Component Analysis (PCA) using Python (Scikit-learn) Step by Step Tutorial: https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60
Views: 49041 Michael Galarnyk
Lecture 14 | Machine Learning (Stanford)
 
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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
Views: 95459 Stanford
FACIAL RECOGNITION USING PRINCIPAL COMPONENT ANALYSIS
 
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Submitted towards Course Project for EEL6825 – Pattern Recognition
Views: 88 Ameya Devbhankar
第13講 K-Nearest Neighbor Classifiers / Principal Component Analysis / Face Recognition (A)
 
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【張智星老師:科學計算Scientific computing】 【課程大綱】 L13_A Concept of KNNC Flowchart or KNNC Display Boundary for 1NNC Decision Boundary for 1NNC Characteristics of KNNC Prepocessing/Variants for KNNC Demos by Cleve 1NNC Decision Boundaries 1NNC Distance as Surfaces and Contours Using Prototypes in KNNC Decision Boundaries of Different Classifiers KNNC網頁說明 Principal Component Analysis
Views: 2234 NTHUOCW
StatQuest: PCA in Python
 
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You asked for it, you got it! Now I walk you through how to do PCA in Python, step-by-step. It's not too bad, and I'll show you how to generate test data, do the analysis, draw fancy graphs and interpret the results. If you want to download the code, it's here: https://statquest.org/2018/01/08/statquest-pca-in-python/ For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/
Face recognition using Principal Component Analysis(PCA) in Matlab - Part 2.2( II )
 
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Theory Part : http://eranugarg12.blogspot.in/p/face-recognition.html Digital image processing using Matlab
Views: 1025 Anamika Aggarwal
3.2 Principal Component Analysis (PCA) | 3 Dimensionality Reduction | Pattern Recognition Class 2012
 
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The Pattern Recognition Class 2012 by Prof. Fred Hamprecht. It took place at the HCI / University of Heidelberg during the summer term of 2012. Website: http://hci.iwr.uni-heidelberg.de/MIP/Teaching/pr/ Playlist with all videos: http://goo.gl/gmOI6 Contents of this recording: 00:01:10 - Principal Component Analysis (PCA) 00:06:52 - MNIST digits 00:22:50 - Rayleigh–Ritz method 00:37:00 - Laplace regression 00:51:42 - extensions of PCA 00:41:45 - Hebbian learning of PCA 00:52:38 - kernel PCA 00:53:06 - robust PCA 00:53:20 - sparse PCA 00:53:50 - probabilistic PCA 00:55:10 - Singular Value Decomposition (SVD) 01:39:48 - Eigenfaces Syllabus: 1. Introduction 1.1 Applications of Pattern Recognition 1.2 k-Nearest Neighbors Classification 1.3 Probability Theory 1.4 Statistical Decision Theory 2. Correlation Measures, Gaussian Models 2.1 Pearson Correlation 2.2 Alternative Correlation Measures 2.3 Gaussian Graphical Models 2.4 Discriminant Analysis 3. Dimensionality Reduction 3.1 Regularized LDA/QDA 3.2 Principal Component Analysis (PCA) 3.3 Bilinear Decompositions 4. Neural Networks 4.1 History of Neural Networks 4.2 Perceptrons 4.3 Multilayer Perceptrons 4.4 The Projection Trick 4.5 Radial Basis Function Networks 5. Support Vector Machines 5.1 Loss Functions 5.2 Linear Soft-Margin SVM 5.3 Nonlinear SVM 6. Kernels, Random Forest 6.1 Kernels 6.2 One-Class SVM 6.3 Random Forest 6.4 Random Forest Feature Importance 7. Regression 7.1 Least-Squares Regression 7.2 Optimum Experimental Design 7.3 Case Study: Functional MRI 7.4 Case Study: Computer Tomography 7.5 Regularized Regression 8. Gaussian Processes 8.1 Gaussian Process Regression 8.2 GP Regression: Interpretation 8.3 Gaussian Stochastic Processes 8.4 Covariance Function 9. Unsupervised Learning 9.1 Kernel Density Estimation 9.2 Cluster Analysis 9.3 Expectation Maximization 9.4 Gaussian Mixture Models 10. Directed Graphical Models 10.1 Bayesian Networks 10.2 Variable Elimination 10.3 Message Passing 10.4 State Space Models 11. Optimization 11.1 The Lagrangian Method 11.2 Constraint Qualifications 11.3 Linear Programming 11.4 The Simplex Algorithm 12. Structured Learning 12.1 structSVM 12.2 Cutting Planes
Views: 28012 UniHeidelberg
CompX: Mathematics of PCA - Covariance matrices
 
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Computational Thinking and Big Data is part of the Big Data MicroMasters program offered by The University of Adelaide and edX. Learn the core concepts of computational thinking and how to collect, clean and consolidate large-scale datasets. Enrol now! http://bit.ly/2rfZXSz
Lec-32 Introduction to Principal Components and Analysis
 
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Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 95631 nptelhrd
Principal Component Analysis- Part II
 
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We realize PCA on a single image for dimension reduction and hence compression.
Views: 7916 rashi agrawal
2. A Complete Understanding of PCA(Principal Component Analysis)
 
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CODE : https://github.com/Parveshdhull/FaceRecognitionUsing-PCA-2D-PCA-And-2D-Square-PCA A Complete Understanding of Singular Value Decomposition https://www.youtube.com/watch?v=GnZQ704UDvM Face Recognition Using PCA(Principal Component Analysis) https://www.youtube.com/playlist?list=PLgWKOWHJlDUM_cog-ujJgYoRCJ6LcAhtU 1. How Face Recognition Works - FaceRecognition Tutorials https://youtu.be/jDduLfZJMGY 2. A Complete Understanding of PCA(Principal Component Analysis) https://youtu.be/NP5zbFLcQYM 3. Face Recognition using PCA - Process https://youtu.be/kLh9JFDD7DY 4. Dataset Class - FaceRecognition Tutorials https://youtu.be/R_2J0p7z-dM 5. Image to Matrix Conversion Class - FaceRecognition Tutorials https://youtu.be/C_xJ2bhAamY 6.1 PCA Class - FaceRecognition Tutorials https://youtu.be/5agps0edurE 6.2 PCA Class(Optional Functions) - FaceRecognition Tutorials https://youtu.be/gy76u09gtz0 7. Face Recognition Class - FaceRecognition Tutorials https://youtu.be/kTIus49zohc 8. Face Recognition in Group Image or Video https://youtu.be/UGHBk7mDzsA 9. Results Comparison of PCA, 2D-PCA, 2D(Square)-PCA, LDA https://youtu.be/FnZExs1MkY0 ORL Dataset https://drive.google.com/drive/folders/1XmSC0zlO6it-GlIGaCHPbeRA_3H9yaSx?usp=sharing Like Page www.facebook.com/right2trick Follow us on https://twitter.com/Right2Trick Google+ https://plus.google.com/107319661580304996377 Share Channel https://www.youtube.com/c/Right2Trick Contact Us [email protected]
Views: 95 Right2Trick
MATLAB CODE for FACE RECOGNITION using Principal Component Analysis PCA
 
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Face Recognition using MATLAB. Simple code for understanding. Please do comment for further updates. Visit: https://matlabcastor.blogspot.com/ Please follow us: https://www.facebook.com/matlabcodes Join us on Telegram: https://t.me/joinchat/Hu4Nk1EMjOMZ223kakzSWQ Join us on Facebook Group: https://www.facebook.com/groups/matlabcodes
Views: 800 Castor Classes
EEL6825-Face Recognition using Principal Component Analysis(Eigenfaces)
 
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The Project is part of my curriculum in EEL6825-Pattern Recognition at University of Florida.
Views: 182 Guneet Singh
Mod-02 Lec-21 Principal Component Analysis (PCA)
 
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Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 5944 nptelhrd
Mod-10 Lec-37 Feature Selection and Dimensionality Reduction; Principal Component Analysis
 
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Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IISc Bangalore. For more details on NPTEL visit http://nptel.ac.in
Views: 4228 nptelhrd
Linear Discriminant Analysis (LDA)  vs Principal Component Analysis (PCA)
 
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LDA vs PCA side by side --------------------------- Read more about Market Basket Analysis and Linear Discriminant Analysis https://www.udemy.com/market-basket-analysis-linear-discriminant-analysis-with-r/?couponCode=MB_LDA_01 ------------------------------
Views: 11275 Gopal Malakar
Rumman Chowdhury, "Deep Dive into Principal Components Analysis", PyBay2016
 
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Commonly used in image recognition, speech to text and text analysis, Principal Components Analysis (or PCA) separates the signal from the noise in your data and reduces your dimensionality so that meaningful analyses can be performed. ​ Abstract PCA is vital for reducing high dimensional models with sparsity issues, without sacrificing the information contributed by each feature. In this talk, I will be explaining what happens under the hood during PCA, making the code and math accessible and interpretable. Bio Rumman comes to data science from a quantitative social science background. Prior to joining Metis, she was a data scientist at Quotient Technology, where she used retailer transaction data to build an award-winning media targeting model. Her industry experience ranges from public policy, to economics, and consulting. Her prior clients include the World Bank, the Vera Institute of Justice, and the Los Angeles County Museum of the Arts. She holds two undergraduate degrees from MIT, a Masters in Quantitative Methods of the Social Sciences from Columbia, and she is currently finishing her Political Science PhD from the University of California, San Diego. Her dissertation uses machine learning techniques to determine whether single-industry towns have a broken political process. Her passion lies in teaching and learning from teaching. In her spare time, she teaches and practices yoga, reads comic books, and works on her podcast. https://speakerdeck.com/pybay/2016-rumman-chowdhury-deep-dive-into-principal-components-analysis
Views: 546 SF Python
第13講 K-Nearest Neighbor Classifiers / Principal Component Analysis / Face Recognition (B)
 
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【張智星老師:科學計算Scientific computing】 【課程大綱】 L13_B Steps for PCA Tidbits Example of PCA Weakness of PCA Linear Discriminant Analysis PCA for Face Recognition Face Recognition ATT Face Dataset Face Recognition via PCA Steps of Feature Extraction via PCA 作業說明
Views: 1835 NTHUOCW
Face recognition using PCA
 
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This video is about face recognition using PCA... Contact Mobile Number: +91-9637253197 Whatsup Number: +91-9637253197 Email ID: [email protected]
Views: 435 Arjun Nichal
Dimensionality reduction Methods in Hindi | Machine Learning Tutorials
 
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Views: 24193 Last moment tuitions