Talk:Vector quantization
'Some math'
An expression occurring in existential sentences. "For some x" is the same as " exists x." Unlike in everyday language, it is does not necessarily refer to a plurality of elements, and so might be more clearly represented in colloquial English as "for at least one." (Turkialjrees (talk) 16:44, 14 March 2015 (UTC)).
During some of my colleges I got some math what could be nice to be on this page. only I don't have enough mathimatical background to prove the used maths.
The Math
create set of prototypes = the data =
by using the Squared_Euclidean_Distance we can determine the multidimention distance between a prototype and a data point. Based on this we can find the closest prototype to a given datapoint. assign to prototype
This way the winner takes it all and the closest prototype should be moved using:
where is the learning rate
Spidfire (talk) 15:29, 31 January 2013 (UTC)
Untitled
also want to see pictures —Preceding unsigned comment added by 138.246.7.74 (talk) 13:50, 15 July 2010 (UTC)
![]() | This article may be too technical for most readers to understand.(September 2010) |
Damn. This article made me feel dumb. --NoPetrol 06:41, 24 Nov 2004 (UTC)
I have modified the article to give a clear explanation of what vector quantization is, together with some uses for it. It still needs tidying up and referencing Pog 21:46, 1 August 2007 (UTC)
Unclear sentence
- "Find the quantization vector centroid with the smallest <distance-sensitivity>"
What does "<distance-sensitivity>" mean? Does it mean sensitivity? Or does it mean distance minus sensitivity? -Pgan002 00:17, 18 August 2007 (UTC)
Spam
Why the hell is there a picture of an aeroplane on this page? —Preceding unsigned comment added by Criffer (talk • contribs) 16:24, 11 October 2007 (UTC)
Use in data compression
"All possible combinations of the N-dimensional vector [y1,y2,...,yn] form the Gaurav."
What the hell is a Gaurav?
Secondly, even if there is a correct technical term for all possible combinations of an N-Dimensional vector, it is completely out of context in that particular article. It should be removed, or correct and given a context. —Preceding unsigned comment added by 198.151.130.16 (talk) 21:46, 1 April 2011 (UTC)
Where is a block diagram?
From the article: Block Diagram: A simple vector quantizer is shown below Huh? Where is it? Cuddlyable3 (talk) 09:15, 7 June 2011 (UTC)
Each cluster the same number of points?!
"It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them."
This is not true, isn't it? E.g. clustering a 1-d normally distributioned data (10k samples) with k-means (6 clusters) results in groups with very different numbers of points assigned to each group (700 to 2400). I would not call this difference "approximately the same". Or am i missing something?
=
No mention of LBG or other methods
Article's "alternate training" method seems biased towards simulated annealing. No mention is made at all of the Linde–Buzo–Gray algorithm which is a fundamental starting point for most VQ implementations and is the most widely-cited paper in VQ work. No mention is made of PNN (Pair Nearest Neighbor) or other codebook generation methods either. --Trixter (talk) 19:49, 26 August 2013 (UTC)
Agreed! The LBG algorithm is fundamental for the topic, Vector Quantization. This, and other code-book generation methods, need to be referenced/linked. Although I have some experience with VQ, I am not an expert in VQ, so am not confident to update the page... Hydradix (talk) 07:43, 5 October 2014 (UTC)
update
I decided to be bold, and added in-page links to LBG and K-Means... I also added LBG to the References.... I tried/wanted to add Enhanced LBG to External References, but when I tired Wikipedia Preview the link would always fail (http://anale-informatica.tibiscus.ro/download/lucrari/2-1-02-balint.pdf) so ELBG was not referenced. — Preceding unsigned comment added by Hydradix (talk • contribs) 08:34, 5 October 2014 (UTC)