Mudit Garg

San Francisco Bay Area Contact Info
910 followers 500+ connections

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Experience & Education

  • Oracle

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Licenses & Certifications

Courses

  • Algorithm Analysis

    CSE 565

  • Computer Architecture

    CSE 530

  • Computer Vision II

    CSE 586

  • Data Mining

    IST 557

  • Data Structures and Algorithms

    CMPSC 465

  • Database Systems

    CSE 541

  • Deep Learning

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  • Digital Image Processing II

    CSE 585

  • Language Based Security

    CSE 597

  • Medical Image Reconstruction

    CSE 597

  • Numerical Linear Algebra

    CSE 550

  • Psychology

    Psych 101

  • Theory of Computation

    CMPSC 464

Projects

  • Spatial Outlier Detection

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    Spatial outliers are objects which have behavioral attribute values that are distinct from those of their surrounding spatial neighbors. In this work , we have used Earth Science data which includes the land cover types at different spatial locations. We have achieved spacial outlier detection with the help of two data mining techniques : Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

    The analysis was preformed on a temporal series of Normalized Difference…

    Spatial outliers are objects which have behavioral attribute values that are distinct from those of their surrounding spatial neighbors. In this work , we have used Earth Science data which includes the land cover types at different spatial locations. We have achieved spacial outlier detection with the help of two data mining techniques : Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

    The analysis was preformed on a temporal series of Normalized Difference Vegetation Index (NDVI) which were produced using data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra satellite. The PCA was used as a data transform to enhance regions of localized change in multi-temporal data sets. This is a direct result of the high correlation that exists among images for regions that do not change significantly and the relatively low correlation associated with regions that change substantially. The LDA was used as a data transform to project multi-temporal dataset onto a lower-dimensional space with good class-separability in order to avoid over-fitting ("curse of dimensionality") and also reduce computational costs.

    Our findings suggest that both PCA and LDA can provide valuable information for detecting anomalies in a spatial data which can be used for environmental management policies involving biodiversity preservation and rational exploitation of natural and agricultural resources.

    Other creators

  • Spatial Outlier Detection

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    Other creators

  • Augmented Reality Viewer

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    See project

  • Health Care Management System

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    See project

  • Obfuscation Techniques in Java

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  • Siamese-RPN with Mean Shift and Feature Engineering

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    See project

Languages

  • English

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  • Hindi

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