Mastering Dimensionality Reduction Techniques in Bioinformatics
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Questions and Answers

Which of the following is a reason why we need dimensionality reduction?

  • To reduce the number of features in a dataset (correct)
  • To increase the interpretability of data
  • To eliminate redundant and irrelevant features
  • To improve data visualization
  • Which of the following is an approach for dimensionality reduction?

  • Feature selection (correct)
  • Supervised learning
  • Data augmentation
  • Clustering
  • Which dimensionality reduction technique is based on finding the directions of maximum variance in a dataset?

  • Singular value decomposition (SVD)
  • Feature extraction
  • Multi-dimensional scaling (MDS)
  • Principal Component Analysis (PCA) (correct)
  • Which of the following datasets has the highest dimensionality?

    <p>A gene expression microarray with 100 samples and 27,000 genes</p> Signup and view all the answers

    What are some costs associated with high dimensionality in datasets?

    <p>Data interpretation and visualization</p> Signup and view all the answers

    Which of the following is NOT an approach for dimensionality reduction?

    <p>Data visualization</p> Signup and view all the answers

    What is the dimensionality of the dataset mentioned in the text?

    <p>27,000</p> Signup and view all the answers

    Which dimensionality reduction technique is based on finding the directions of maximum variance in a dataset?

    <p>Principal Component Analysis (PCA)</p> Signup and view all the answers

    Which of the following datasets has the highest dimensionality?

    <p>Genomics with thousands of genes</p> Signup and view all the answers

    What are some costs associated with high dimensionality in datasets?

    <p>Data interpretation and visualization</p> Signup and view all the answers

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