Dimension Reduction
data processing
Overview
Use casereducing the number of variables in datasets while preserving important information
Also see
Knowledge graph stats
Claims14
Avg confidence93%
Avg freshness99%
Last updatedUpdated 4 days ago
WikidataQ1780371
Trust distribution
100% unverified
Governance
Not assessed
Dimension Reduction
concept
Technique for reducing vector dimensions while preserving important information
Compare with...includes technique
| Value | Trust | Confidence | Freshness | Sources |
|---|---|---|---|---|
| Principal Component Analysis (PCA) | ○Unverified | High | Fresh | 1 |
| t-Distributed Stochastic Neighbor Embedding (t-SNE) | ○Unverified | High | Fresh | 1 |
| Linear Discriminant Analysis (LDA) | ○Unverified | High | Fresh | 1 |
implemented in
| Value | Trust | Confidence | Freshness | Sources |
|---|---|---|---|---|
| scikit-learn | ○Unverified | High | Fresh | 1 |
| TensorFlow | ○Unverified | High | Fresh | 1 |
| PyTorch | ○Unverified | High | Fresh | 1 |
used for
| Value | Trust | Confidence | Freshness | Sources |
|---|---|---|---|---|
| machine learning preprocessing | ○Unverified | High | Fresh | 1 |
| data visualization | ○Unverified | High | Fresh | 1 |
| curse of dimensionality mitigation | ○Unverified | High | Fresh | 1 |
primary use case
| Value | Trust | Confidence | Freshness | Sources |
|---|---|---|---|---|
| reducing the number of variables in datasets while preserving important information | ○Unverified | High | Fresh | 1 |
based on
| Value | Trust | Confidence | Freshness | Sources |
|---|---|---|---|---|
| linear algebra and statistical methods | ○Unverified | High | Fresh | 1 |
integrates with
| Value | Trust | Confidence | Freshness | Sources |
|---|---|---|---|---|
| NumPy | ○Unverified | High | Fresh | 1 |
| pandas | ○Unverified | Moderate | Fresh | 1 |
requires
| Value | Trust | Confidence | Freshness | Sources |
|---|---|---|---|---|
| numerical data | ○Unverified | Moderate | Fresh | 1 |