Machine Learning Philosophy Idea – All Data Has A Direction Like A[Concentrated, Pointing, Diffuse, Good, Better, …

Just wanted to share some philosophy.

Inspired by my network theory. Water refractive index is data that can be directed.

From this I gather that. All data can be split into ”word direction vectors”. Be it data = [dataX, dataY , dataZ, dataGood, dataBetter, dataBest, dataDiffuse,dataConcentrated,dataPointing, … ] or something more.

So in machine learning I guess that it could be an idea to split data in more than the obvious groups.

?One way to split data I think is to have one network for each word direction. That is. Let the networks corresponding to each word compete or compare the usage they make of the data samples.

Thinking about this further. Maybe if I have input as output my weights in my ?dynamically expanding network function can be my cluster groups. Here the shape of the network is important. At least they could be related to clusters. Similar to my physical network theory.

I will have to test if this method is something.

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