'Dimensionality reduction' presentation slideshows

Dimensionality Reduction for Data Mining - Techniques, Applications and Trends

Dimensionality Reduction for Data Mining - Techniques, Applications and Trends. Lei Yu Binghamton University Jieping Ye, Huan Liu Arizona State University. Outline. Introduction to dimensionality reduction Feature selection (part I) Basics Representative algorithms Recent advances

1.87k views • 135 slides

MINING & WAREHOUSING (MSS2) BY CHANDRA S. AMARAVADI

MINING & WAREHOUSING (MSS2) BY CHANDRA S. AMARAVADI. EXTENSIONS TO DSS. BI systems (aka EIS) Geographical Information Systems (GIS) Collaborative Systems (formerly GDSS) Expert Systems OLAP/Data mining/warehousing. DATA WAREHOUSES. DATA WAREHOUSE.

796 views • 63 slides

Data Pre-processing

Data Pre-processing. Data cleaning Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies Data integration Integration of multiple databases, data cubes, or files Data transformation Normalization and aggregation Data reduction

738 views • 55 slides

Anomaly detection and sequential statistics in time series

Anomaly detection and sequential statistics in time series. Alex Shyr CS 294 Practical Machine Learning 11/12/2009 (many slides from XuanLong Nguyen and Charles Sutton). Two topics. Anomaly detection. Sequential statistics. Review. Dimensionality Reduction e.g. PCA HMM ROC curves.

1.33k views • 51 slides

Chapter 4 Data Preprocessing

Chapter 4 Data Preprocessing. To make data more suitable for data mining. To improve the data mining analysis with respect to time, cost and quality. Why Data Preprocessing?. Data in the real world is dirty

743 views • 47 slides

15-853:Algorithms in the Real World

15-853:Algorithms in the Real World. Indexing and Searching I (how google and the likes work). Indexing and Searching Outline. Introduction: model, query types Inverted File Indices: Compression, Lexicon, Merging Vector Models: Latent Semantic Indexing:

588 views • 46 slides

Statistical Tools for Multivariate Six Sigma

Statistical Tools for Multivariate Six Sigma. Dr. Neil W. Polhemus CTO & Director of Development StatPoint, Inc. Revised talk: www.statgraphics.comdocuments.htm. The Challenge. The quality of an item or service usually depends on more than one characteristic.

685 views • 44 slides

Fall 2004, CIS, Temple University CIS527: Data Warehousing, Filtering, and Mining Lecture 3 Data Warehousing and OLAP Te

Fall 2004, CIS, Temple University CIS527: Data Warehousing, Filtering, and Mining Lecture 3 Data Warehousing and OLAP Technology for Data Mining Lecture slides taken/modified from: Vipin Kumar ( http://www-users.cs.umn.edu/~kumar/csci5980/index.html ). What is Data?.

518 views • 44 slides

Speech and Image Processing Unit Department of Computer Science University of Joensuu, FINLAND

591 views • 43 slides

Information Theoretic Clustering, Co-clustering and Matrix Approximations Inderjit S. Dhillon Uni

Information Theoretic Clustering, Co-clustering and Matrix Approximations Inderjit S. Dhillon University of Texas, Austin. Joint work with A. Banerjee, J. Ghosh, Y. Guan, S. Mallela,

705 views • 39 slides

Hadoop

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624 views • 34 slides

ISOMAP TRACKING WITH PARTICLE FILTER

ISOMAP TRACKING WITH PARTICLE FILTER. Presented by Nikhil Rane. Dimensionality Reduction. Let x i be H-dimensional and y i be L-dimensional then dimensionality reduction solves the problem x i = f (y i ) where H>L. Dimensionality Reduction Techniques. Linear PCA

510 views • 32 slides

539 views • 28 slides

Chapter 8. Principal-Components Analysis Neural Networks and Learning Machines ( Haykin )

822 views • 25 slides

Covariance Matrix Applications

Covariance Matrix Applications. Dimensionality Reduction. Outline. What is the covariance matrix? Example Properties of the covariance matrix Spectral Decomposition Principal Component Analysis. Covariance Matrix.

1.24k views • 24 slides

Non-linear Dimensionality Reduction

Non-linear Dimensionality Reduction. CMPUT 466/551 Nilanjan Ray Prepared on materials from the book Non-linear dimensionality reduction By Lee and Verleysen , Springer, 2007. Agenda. What is dimensionality reduction? Linear methods Principal components analysis

449 views • 24 slides

Tutorial 11 Constrained optimization Lagrange Multipliers KKT Conditions

Tutorial 11 Constrained optimization Lagrange Multipliers KKT Conditions. Constrained Optimization. Constrained optimization problem can be defined as following: Minimize the function, while searching among x , that satisfy the constraints:.

2.81k views • 18 slides

Multiplicative updates for the LASSO

342 views • 17 slides

NIPS 2001 Workshop on Feature/Variable Selection Isabelle Guyon BIOwulf Technologies

NIPS 2001 Workshop on Feature/Variable Selection Isabelle Guyon BIOwulf Technologies. Schedule. 7:30-8:00: Welcome and introduction to the problem of feature/variable selection - Isabelle Guyon - 8:00-8:20 a.m. Dimensionality Reduction via Sparse Support Vector Machines - Jinbo Bi,

315 views • 16 slides