CODEWITHSUNDEEP
machine-learning
Trung
Trung

Reputation: 31

Can feature in machine-learning be matrix?

As you know, the feature in Iris dataset is 1 dimensional array for example : 

[5.1, 3.5, 1.4, 0.2]  Iris-Setosa  
[7.0, 3.2, 4.7, 1.4]  Iris-versicolor

I'm wondering if feature in machine-learning can be 2d-arrays or not ?
For example: 

Feature                            Result  
[[1.0  2.3  3.1],[2.4  6.3  9.6]]  A  
[[1.5  3.3  5.1],[5.4  9.3  7.0]]  B

Thanks you!

Upvotes: 2

Views: 974

Answers (1)

Vivek Kalyanarangan
Vivek Kalyanarangan

Reputation: 9081

Yes, they can be. In this case, the input become Tensors.

0 dimensional input - scalars

1 dimensional input - vectors

2 dimensional input - matrices

3 (and above) dimensional input - tensors

An image for example is a (m x n x p) tensor with RGB components. So each input is a multi-dimensional array of numbers. It is a matrix of matrices - This is an excellent explanation

Word embeddings are also somewhat similar. Words make up a document. A corpus is a collection of documents. In this case each word is typically 1 300-number vector. So a document will be a matrix - so each input is a matrix - Start here

Upvotes: 1

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