Essentials of Non-negative Matrix Factorization Quiz Quiz

Explore the fundamental concepts of Non-negative Matrix Factorization (NMF) with this easy quiz. Designed for learners seeking to understand NMF basics, key properties, applications, and core terminology in data analysis and machine learning.

1. Purpose of NMF

   What is the main goal of Non-negative Matrix Factorization when applied to a dataset in matrix form?

   1. To increase the size of the original matrix
   2. To randomly shuffle the matrix values
   3. To create a negative matrix from the data
   4. To decompose the data into non-negative factors

   Explanation: NMF aims to break down a matrix into two or more smaller matrices with only non-negative entries, revealing useful features or patterns. Increasing matrix size is not its purpose, and creating negative matrices goes against the non-negative requirement. Randomly shuffling values is unrelated to the factorization process.

2. NMF Requirements

   Which type of matrix can be factorized using Non-negative Matrix Factorization?

   1. A matrix with only negative entries
   2. A matrix with only non-negative entries
   3. A matrix with only zeros
   4. A matrix with both very large and very small values

   Explanation: NMF is specifically designed for matrices that contain only non-negative (zero or positive) values, which is essential for its mathematical foundation. While large or small values do not matter as long as they are non-negative, matrices with negatives or all-zero entries are not typical for NMF. Matrices with just zeros would not reveal meaningful patterns.

3. Resulting Matrices

   After applying NMF to an m×n data matrix V, what are the typical names of the resulting two factor matrices?

   1. Q and R
   2. A and B
   3. C and D
   4. W and H

   Explanation: The standard notation for the factor matrices in NMF is W and H, where V is approximately equal to W multiplied by H. A and B, Q and R, and C and D are not conventional for NMF, though such symbols could be used in other contexts or factorizations.

4. Feature Extraction with NMF

   How does Non-negative Matrix Factorization help in feature extraction for text data analysis?

   1. It encrypts the data
   2. It produces interpretable parts-based representations
   3. It sorts the matrix rows alphabetically
   4. It removes all duplicate features

   Explanation: NMF is valued for generating parts-based and easily interpretable representations, which is particularly useful for understanding topics in text analysis. It does not encrypt data or remove duplicates directly, and sorting rows alphabetically is unrelated to its function.

5. NMF Constraints

   What key constraint is enforced in Non-negative Matrix Factorization on all the entries of the factorized matrices?

   1. All entries must be zero or positive
   2. All entries must be negative
   3. All entries must sum to one
   4. All entries must be integers

   Explanation: NMF strictly requires that every entry in both factorized matrices is zero or a positive number. Negatives are not allowed, so 'all entries must be negative' is incorrect. The entries do not have to sum to one nor be integers, making those options wrong as well.

6. NMF in Image Processing

   Why is Non-negative Matrix Factorization commonly used in image processing tasks?

   1. It can blur the image backgrounds
   2. It inverts the image colors automatically
   3. It removes pixels with zero intensity
   4. It can detect and separate distinct image components

   Explanation: NMF helps in revealing meaningful image components, such as separating faces or objects, due to its non-negative constraint. It does not inherently blur backgrounds, remove zero pixels, or invert image colors, which are separate image processing tasks.

7. Rank Selection in NMF

   In NMF, selecting the rank (number of components) primarily affects which aspect of the resulting factorization?

   1. The running time only
   2. The number of extracted features
   3. The matrix entry signs
   4. The original data size

   Explanation: Choosing the rank in NMF determines how many features or components will be extracted from the data. It does not affect entry signs, as all must remain non-negative. Running time might increase with higher rank, but the main influence is on feature quantity, not the original data size.

8. Non-negativity in NMF

   Why is the non-negativity constraint considered important in many NMF applications?

   1. It simplifies mathematical calculations only
   2. It leads to more interpretable features in the real world
   3. It makes matrices always square
   4. It guarantees minimal computation time

   Explanation: Non-negativity tends to yield features that correspond to additive, understandable parts in real-world scenarios, such as objects or topics. While it can simplify some mathematics, that's not the main reason for its importance. NMF does not require square matrices, nor does non-negativity guarantee minimal computation time.

9. Comparison with PCA

   What is a key difference between Non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA)?

   1. NMF requires data normalization, while PCA does not
   2. NMF does not allow negative values in its components, while PCA can
   3. PCA is only for image data, while NMF is not
   4. PCA always outperforms NMF in clustering tasks

   Explanation: A fundamental difference is that NMF restricts all matrix entries to zero or positive values, unlike PCA where negative values are possible. Both can be applied to various data types besides images, and performance in clustering depends on the problem, making the other options inaccurate.

10. NMF Initialization Methods

    Which approach is commonly used to initialize the factor matrices in NMF algorithms?

    1. Starting with all negative values
    2. Using prime numbers only
    3. Zeroing out all entries in both matrices
    4. Assigning random non-negative values

    Explanation: Random non-negative initialization is frequently used to begin the iterative process of NMF, helping to avoid local minima. Negative or zero-only initializations are not suitable due to the non-negative constraint and computational limitations. Prime number initialization is not a standard practice.