PR-Feature Extraction

Choose the qualitative feature of a pattern recognition system.

• Shape of the face

• Heart rate in beats per minute

• Width of the nose in pixels

• Amplitude of a sound wave

Choose the feature vector components of a facial recognition system.

• Shape, color, and texture of facial features

• Frequency of sound waves

• Document word count

• Stock market trends

Evaluate the reason for dimensionality reduction needed in high-dimensional datasets.

• To prevent the curse of dimensionality

• To increase the number of features

• To complicate machine learning algorithms

• To reduce training efficiency

Choose an example of qualitative feature for sound analysis.

• Sound category

• Amplitude in decibels

• Frequency bands

• Duration in seconds

Choose the inappropriate feature for ECG analysis.

• Language type

• QRS duration

• T-wave amplitude

• PR interval

Choose the method for reducing redundancy in feature extraction.

• Principal Component Analysis

• Sequential Backward Elimination

• Kernel PCA

• Genetic Algorithms

Choose the feature vector components for a text analysis system.

• Sentence length

• Eye color

• Pixel intensity

• ECG amplitude

Analyze the concept of feature selection to determine its primary purpose in a pattern recognition system.

• Reducing computational complexity

• Increasing data redundancy

• Generating new features

• Ignoring raw data features

Choose the best description of shape features in structural pattern recognition.

• To recognize qualitative data

• As inputs for multi-resolution analysis

• For identifying irrelevant features

• To reduce training data

Choose a crucial feature extraction method for biometric applications.

• Identifying measurable properties like face width and height

• Ignoring facial landmarks

• Selecting features with low relevance

• Randomly sampling features