A question about multiresolution analysis (from a topological point of view)

In summary, the conversation discusses a problem understanding a concept in a book about multiresolution analysis. The question is whether the countable union of subspaces is equal to or only dense in L2(R). The person initially thought they were equivalent, but after reading the article, they understand that "dense" is the correct term. They also make a comparison to the concept of limits in calculus.
  • #1
Lajka
68
0
Hi,

I have a problem understanding something

This is a snapshot of a book I am reading

NfBL7.png


Point no. 2 concerns me, because it looks to me like it contradicts itself, with "this or this"

The first part says

[itex]\sum_{j}V_j = \mathbb{L^2(R)}[/itex] which, to me, looks completely equivavalent to
[itex]\lim_{j \rightarrow \infty}V_j = \mathbb{L^2(R)}[/itex]
given the nested nature of these subspaces.

However, the paper says
1F4KF.png


so what troubles me is this: is this countable union [itex]\sum_{j}V_j[/itex] equal to [itex]\mathbb{L^2(R)}[/itex] or is it only dense in [itex]\mathbb{L^2(R)}[/itex]?

I personally think it's the former, and I don't understand this "dense" part. Could someone perhaps clarify this for me?

Much obliged!
 
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  • #3
Yes, it does. I had trouble understanding it, but I think I make some progress. I actually think now I may got it wrong from the very beginning.

E.g., if you have a family of functions {f_n} that has a limit f, I think it's okay to say that "lim(n→∞)=f" as well as "family {f_n} can approach arbitrarily close to f", because it's basically the same statement.

In the same manner, I now think it's equivalent to say "lim(n→∞) V_n = L2 (R)" or "union V_n can approach arbitrarily close to L2 (R)".
 

Related to A question about multiresolution analysis (from a topological point of view)

1. What is multiresolution analysis?

Multiresolution analysis is a mathematical tool used to analyze data in a hierarchical manner at multiple levels of resolution. It is commonly used in signal processing and image analysis to decompose data into different frequency or scale components.

2. How does multiresolution analysis work?

Multiresolution analysis works by breaking down a signal or image into smaller and smaller components, each representing a different level of resolution. This is often done using a series of filters and downsampling techniques to extract the most relevant information at each level.

3. What is the role of topology in multiresolution analysis?

Topology, the study of geometric properties that are preserved under continuous deformations, plays a crucial role in multiresolution analysis. It provides a framework for understanding the relationships between different levels of resolution and how they are connected.

4. What are the advantages of using multiresolution analysis?

One of the main advantages of multiresolution analysis is its ability to extract and analyze data at multiple levels of detail, allowing for a more comprehensive understanding of the data. It also allows for efficient compression and denoising of signals and images.

5. What are some applications of multiresolution analysis?

Multiresolution analysis has many applications, including image and signal processing, data compression, computer graphics, and pattern recognition. It is also used in fields such as geology, biology, and finance for data analysis and visualization.

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