# Intelligent Feature Selection (with SVM or PCA)

**URL:** <https://discourse.flucoma.org/t/intelligent-feature-selection-with-svm-or-pca/788>\
**Category:** Code Sharing\
**Created:** [February 23, 2021, 12:59pm UTC](https://discourse.flucoma.org/t/intelligent-feature-selection-with-svm-or-pca/788 "2021-02-23T12:59:26Z")\
**Posts on this page:** 1\
**Showing post:** 41

<div class="post-metadata">

**Author:** ![rodrigo.constanzo](https://discourse.flucoma.org/user_avatar/discourse.flucoma.org/rodrigo.constanzo/32/12_2.png) [@rodrigo.constanzo](https://discourse.flucoma.org/u/rodrigo.constanzo)\
**Post date:** [March 3, 2021, 9:31pm UTC](https://discourse.flucoma.org/t/intelligent-feature-selection-with-svm-or-pca/788/41 "2021-03-03T21:31:42Z")

</div>

> [@tedmoore](#):
>
> I just checked your patch. The math on the values part looks right!

That seems a lot more sensible and what I would expect, so something’s gone funny in the code/math somewhere.

Does the squaring/weighting produce this for you?

```auto
0.547614 0.138316 0.081888 0.062887 0.031371 0.022768 0.02044 0.01675 0.013668 0.011574 0.009137 0.008754 0.007181 0.006379 0.005358 0.004786 0.004126 0.003053 0.002383 0.00116 0.000405

```

I don’t see how multiplying those numbers by the matrix (unless the matrix itself heavily skews another way) that I wouldn’t end up with what I did in the end for mine (a sequence of items in the order that they are listed in the `values` field.

> [@tremblap](#):
>
> Derivatives are one way of representing time. At the moment i prefer curated time slots as you saw in my APT and other patches. I’m not finished with this at all 🙂 I’ll report later.

Yeah, curious how you get on with that. With my approach, I don’t so much have the luxury of time as my real-world waiting period is 256 samples (so even a single derivative is kind of pushing it there…) and the longer (predicted) window is 4410 samples, so again, not a lot of meat on those bones. I guess I can further segment the 4410, but I’m not entirely sure what I’ll find there (in terms of analyzing the sounds coming from my real-time input).

The reason I’m more interested in morphologies/gestures is that if they are separable from (absolute) durations, I could potentially query a much longer sample for a similar overall morphology regardless of it being 500ms or 5s long.

> [@tremblap](#):
>
> How long is a piece of string? 😃 Seriously, this is the biggest question of ML. It relies on high-quality human labelling to train. What makes it good is its accuracy, so @danieleghisi approved audio-to-naming is the best !

If that’s the case I’ve got oodles of “labelled” files. Now how much that corresponds to sounding results is yet to be determined (e.g. hitting a fork vs hitting a spoon).

> [@tremblap](#):
>
> indeed that is helpful and also helped us to do the other examples on that paper. Drums are easy to find for that - and an intersting problem because the taxo blurs - a fat snare and a tight tom can start to inhabit the same space.

I was poking at this a couple years ago in [this thread](https://discourse.flucoma.org/t/general-purpose-vs-specific-use-case-or-secret-sauce-and-where-to-get-it/325), about how to optimize some of these algorithms/descriptors/stats/searches if you have known inputs. This is somewhat where I’m with the approach(es) from this patch to try to get the most differentiation for a given set of inputs, but I’m sure there’s way more than that that can be done. I just wouldn’t even know where to begin.

---

_[View the full topic](https://discourse.flucoma.org/t/intelligent-feature-selection-with-svm-or-pca/788)._