K-dimensional manifold problem

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In summary, the conversation discusses proving that the Cartesian product of two manifolds is also a manifold of higher dimension. The hint suggests using the local graph property of manifolds to prove this. The book does not provide a clear definition of a manifold, making it difficult to know where to begin.
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Homework Statement



Suppose X ⊂ R^n is a k-dimensional manifold and Y ⊂ R^p is an l-dimensional manifold. Prove that:

X × Y = {[x,y] ∈ R^n × R^p : x ∈ X and y ∈ Y}

is a (k+l)-dimensional manifold in R^(n+p). (Hint: Recall that X is locally a graph over a k-dimensional coordinate plane in R^n and Y is locally a graph over an l-dimensional coordinate plane in R^p.)

Homework Equations



None known.

The Attempt at a Solution



Our book doesn't even give a really solid definition of a manifold, so I don't really know where to start.
 
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It doesn't give a definition of a manifold? Then how can it ask you to recall "that X is locally a graph over a k-dimensional coordinate plane in R^n and Y is locally a graph over an l-dimensional coordinate plane in R^p"? That's all you need.
 

1. What is a K-dimensional manifold?

A K-dimensional manifold is a mathematical concept that describes a space with K dimensions, where each point in the space can be represented by a set of K coordinates. It is a generalization of a two-dimensional surface in three-dimensional space, and can be visualized as a curved surface embedded in a higher-dimensional space.

2. What is the K-dimensional manifold problem?

The K-dimensional manifold problem refers to the challenge of finding a low-dimensional representation of high-dimensional data. This is important in many fields, such as machine learning and data analysis, where high-dimensional data can be difficult to interpret and analyze. The goal is to find a lower-dimensional representation that preserves the important information and relationships in the data.

3. How is the K-dimensional manifold problem solved?

The K-dimensional manifold problem can be tackled using various mathematical and computational techniques, such as principal component analysis, multidimensional scaling, and t-SNE. These methods aim to reduce the dimensionality of the data while retaining as much of the original information as possible.

4. What are the applications of K-dimensional manifold?

K-dimensional manifold has various applications in different fields, such as computer vision, natural language processing, and data visualization. It can be used to analyze and understand complex datasets, identify patterns and relationships, and make predictions. It is also used in feature extraction and dimensionality reduction for machine learning algorithms.

5. What are the limitations of K-dimensional manifold?

One limitation of K-dimensional manifold is that it relies on the assumption that the data is inherently low-dimensional and can be represented as a smooth, curved surface. This may not always be the case, especially with noisy or sparse data. Additionally, the choice of dimensionality reduction method and the number of dimensions can greatly affect the results and may require some trial and error.

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