Table of Contents
- 1 What does it mean to have sparse data?
- 2 Why is high dimensional data bad?
- 3 What is sparse data in deep learning?
- 4 What is sparse data with example?
- 5 Why high dimensionality is considered as curse in machine learning?
- 6 What is the best distance measure for high dimensional data?
- 7 What is sparse format?
- 8 What is Sparity ML?
What does it mean to have sparse data?
Definition: Sparse data A variable with sparse data is one in which a relatively high percentage of the variable’s cells do not contain actual data. Such “empty,” or NA, values take up storage space in the file.
Why is high dimensional data bad?
An increase in the number of dimensions of a dataset means there are more entries in the vector of features that represents each observation in the corresponding Euclidean space. The variance increases as they get more opportunity to overfit to noise in more dimensions, resulting in poor generalization performance.
What is the sparse data problem?
A common problem in machine learning is sparse data, which alters the performance of machine learning algorithms and their ability to calculate accurate predictions. Data is considered sparse when certain expected values in a dataset are missing, which is a common phenomenon in general large scaled data analysis.
What is sparse data in deep learning?
Difference between missing data and sparse data in machine learning algorithms. machine-learning dataset missing-data sparse. What are main differences between sparse data and missing data?
What is sparse data with example?
Typically, sparse data means that there are many gaps present in the data being recorded. For example, in the case of the sensor mentioned above, the sensor may send a signal only when the state changes, like when there is a movement of the door in a room.
What is a high dimensional data set?
High dimensional data refers to a dataset in which the number of features p is larger than the number of observations N, often written as p >> N. A dataset could have 10,000 features, but if it has 100,000 observations then it’s not high dimensional.
Why high dimensionality is considered as curse in machine learning?
The curse of dimensionality basically means that the error increases with the increase in the number of features. A higher number of dimensions theoretically allow more information to be stored, but practically it rarely helps due to the higher possibility of noise and redundancy in the real-world data.
What is the best distance measure for high dimensional data?
This means that the L1 distance metric (Manhattan Distance metric) is the most preferable for high dimensional applications, followed by the Euclidean Metric (L2), then the L3 metric, and so on.
How is sparse data treated?
Methods for dealing with sparse features
- Removing features from the model. Sparse features can introduce noise, which the model picks up and increase the memory needs of the model.
- Make the features dense.
- Using models that are robust to sparse features.
What is sparse format?
The compressed sparse row (CSR) or compressed row storage (CRS) or Yale format represents a matrix M by three (one-dimensional) arrays, that respectively contain nonzero values, the extents of rows, and column indices. This format allows fast row access and matrix-vector multiplications (Mx).
What is Sparity ML?
In AI inference and machine learning, sparsity refers to a matrix of numbers that includes many zeros or values that will not significantly impact a calculation. For years, researchers in machine learning have been playing a kind of Jenga with numbers in their efforts to accelerate AI using sparsity.