Dimensionality Reduction Techniques_ Understanding PCA in Data Analytics

Nov 21, 2024

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Dimensionality reduction techniques like Principal Component Analysis (PCA) play a critical role in simplifying these datasets without losing valuable information. This technique is particularly useful in scenarios where you need to visualize, interpret, or make predictions with large datasets. For those looking to gain in-depth knowledge of such techniques, the Data Analytics Course Online provides the right foundation for mastering tools like PCA. <br>

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Dimensionality Reduction Techniques_ Understanding PCA in Data Analytics

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Dimensionality Reduction Techniques

Dimensionality Reduction Techniques. Dimitrios Gunopulos, UCR. Retrieval techniques for high-dimensional datasets. The retrieval problem: Given a set of objects S , and a query object S, find the objectss that are most similar to S. Applications:

724 views • 48 slides

Nonlinear Dimensionality Reduction

Nonlinear Dimensionality Reduction. Presented by Dragana Veljkovic. Overview. Curse-of-dimensionality Dimension reduction techniques Isomap Locally linear embedding (LLE) Problems and improvements. Problem description.

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Dimensionality reduction

Dimensionality reduction. Outline. From distances to points : MultiDimensional Scaling (MDS ) FastMap Dimensionality Reductions or data projections Random projections Principal Component Analysis (PCA ). Multi-Dimensional Scaling (MDS).

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Dimensionality Reduction

Dimensionality Reduction. Dimensionality Reduction. High-dimensional == many features Find concepts/topics/genres: Documents: Features: Thousands of words, millions of word pairs Surveys – Netflix: 480k users x 177k movies. Dimensionality Reduction. Compress / reduce dimensionality:

692 views • 42 slides

Dimensionality reduction

Dimensionality reduction. Usman Roshan CS 698 Fall 2013. Supervised dim reduction: Linear discriminant analysis. Fisher linear discriminant: Maximize ratio of difference means to sum of variance. Linear discriminant analysis. Fisher linear discriminant:

603 views • 30 slides

Dimensionality reduction

Dimensionality reduction. Outline. From distances to points : MultiDimensional Scaling (MDS) Dimensionality Reductions or data projections Random projections Singular Value Decomposition and Principal Component Analysis (PCA). Multi-Dimensional Scaling (MDS).

515 views • 36 slides

Dimensionality reduction

Dimensionality reduction. Outline. From distances to points : MultiDimensional Scaling (MDS) FastMap Dimensionality Reductions or data projections Random projections Principal Component Analysis (PCA). Multi-Dimensional Scaling (MDS).

432 views • 27 slides

Dimensionality reduction

Dimensionality reduction. Alexis Boukouvalas Work in collaboration with D. M. Maniyar and D. Cornford. Goals. Develop methods for dimensionality reduction of either the input and/or output space of models. To gain an understanding initially use a toy dataset to compare existing methods.

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Dimensionality Reduction

Dimensionality Reduction. Haiqin Yang. Outline. Dimensionality reduction vs. manifold learning Principal Component Analysis (PCA) Kernel PCA Locally Linear Embedding (LLE) Laplacian Eigenmaps (LEM) Multidimensional Scaling (MDS) Isomap Semidefinite Embedding (SDE) Unified Framework.

512 views • 25 slides

Dimensionality Reduction

Dimensionality Reduction. Given N vectors in n dims, find the k most important axes to project them k is user defined ( k < n ) Applications : information retrieval & indexing identify the k most important features or

578 views • 27 slides

Dimensionality Reduction

Dimensionality Reduction. Random Projections. Johnson-Lindenstrauss lemma For: 0< e < 1/2, any (sufficiently large) set S of M points in R n k = O( e -2 lnM) There exists a linear map f: S  R k , such that (1- e ) ||u-v|| 2 ≤ ||f(u)-f(v)|| 2 ≤ (1+ e )||u-v|| 2 for u,v in S

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Demystifying Dimensionality Reduction

Jeff Hansen Senior Data Engineer. April 2013. Demystifying Dimensionality Reduction. Demystifying Dimensionality Reduction. A Tribute to Johnson and Lindenstrauss. Who is this?. What is this?. How about this?. Hint: It’s for kids…. Some Perspectives are Better than Others.

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Dimensionality reduction

Dimensionality reduction. Usman Roshan. Dimensionality reduction. What is dimensionality reduction? Compress high dimensional data into lower dimensions How do we achieve this?

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CSE 446 Dimensionality Reduction and PCA Winter 2012

CSE 446 Dimensionality Reduction and PCA Winter 2012. Slides adapted from Carlos Guestrin & Luke Zettlemoyer. Machine Learning. Supervised Learning. Unsupervised Learning. Fri K- means & A gglomerative C lustering Mon Expectation Maximization (EM)

576 views • 41 slides

Dimensionality reduction

Dimensionality reduction. Usman Roshan CS 698 Fall 2013. Dimensionality reduction. What is dimensionality reduction? Compress high dimensional data into lower dimensions How do we achieve this?

559 views • 30 slides

Dimensionality Reduction

Dimensionality Reduction. Multimedia DBs. Many multimedia applications require efficient indexing in high-dimensions (time-series, images and videos, etc) Answering similarity queries in high-dimensions is a difficult problem due to “curse of dimensionality”

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Dimensionality reduction

Dimensionality reduction. Usman Roshan. Supervised dim reduction: Linear discriminant analysis. Fisher linear discriminant: Maximize ratio of difference means to sum of variance. Linear discriminant analysis. Fisher linear discriminant:

309 views • 28 slides

Dimensionality reduction

Dimensionality reduction. Usman Roshan. Dimensionality reduction. What is dimensionality reduction? Compress high dimensional data into lower dimensions How do we achieve this?

419 views • 34 slides

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