Orange: Manifold Learning - Revision history

Onnowpurbo: /* Contoh */

2020-04-15T03:55:27Z

Contoh

← Older revision		Revision as of 03:55, 15 April 2020
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	==Contoh==		==Contoh==

	Widget Manifold Learning mengubah data dimensi tinggi menjadi pendekatan dimensi yang lebih rendah. Ini membuatnya bagus untuk memvisualisasikan dataset dengan banyak fitur. Kita menggunakan voting.tab untuk memetakan data 16 dimensi ke grafik 2D. Lalu kita menggunakan widget Scatter Plot untuk memplot embeddings.		Widget Manifold Learning mengubah data dimensi tinggi menjadi pendekatan dimensi yang lebih rendah. Ini membuatnya bagus untuk memvisualisasikan dataset dengan banyak fitur. Kita menggunakan voting.tab dari widget File untuk memetakan data 16 dimensi ke grafik 2D menggunakan widget Manifold Learning. Lalu kita menggunakan widget Scatter Plot untuk memplot embeddings.

	[[File:Manifold-learning-example.png\|center\|600px\|thumb]]		[[File:Manifold-learning-example.png\|center\|600px\|thumb]]

Onnowpurbo: /* Output */

2020-04-15T01:45:00Z

Output

← Older revision		Revision as of 01:45, 15 April 2020
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	Widget Manifold Learning menghasilkan embeddings yang berbeda untuk data high-dimensional.		Widget Manifold Learning menghasilkan embeddings yang berbeda untuk data high-dimensional.

	[[File:Collage-manifold.png\|center\|~~200px~~\|thumb]]		[[File:Collage-manifold.png\|center\|600px\|thumb]]

	Dari kiri ke kanan, atas ke bawah: t-SNE, MDS, Isomap, Locally Linear Embedding dan Spectral Embedding.		Dari kiri ke kanan, atas ke bawah: t-SNE, MDS, Isomap, Locally Linear Embedding dan Spectral Embedding.

Onnowpurbo at 21:53, 13 April 2020

2020-04-13T21:53:16Z

← Older revision		Revision as of 21:53, 13 April 2020
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	Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/manifoldlearning.html		Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/manifoldlearning.html

			Widget Manifold Learning dapat melakukan pengurangan / reduksi dimensi secara nonlinear.

	~~Pengurangan~~ / ~~Reduksi~~ dimensi secara nonlinear.

	==Input==		==Input==
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	Transformed Data: dataset with reduced coordinates		Transformed Data: dataset with reduced coordinates

	Manifold Learning adalah teknik yang menemukan manifold non-linear dalam ruang dimensi yang lebih tinggi. Widget kemudian menampilkan koordinat baru yang sesuai dengan ruang dua dimensi. Data tersebut dapat kemudian divisualisasikan dengan Scatter Plot atau widget visualisasi lainnya.		Widget Manifold Learning adalah teknik yang menemukan manifold non-linear dalam ruang dimensi yang lebih tinggi. Widget Manifold Learning kemudian menampilkan koordinat baru yang sesuai dengan ruang dua dimensi. Data tersebut dapat kemudian divisualisasikan dengan widget Scatter Plot atau widget visualisasi lainnya.

	[[File:Manifold-learning-stamped.png\|center\|200px\|thumb]]		[[File:Manifold-learning-stamped.png\|center\|200px\|thumb]]
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	==Contoh==		==Contoh==

	Widget Manifold Learning mengubah data dimensi tinggi menjadi pendekatan dimensi yang lebih rendah. Ini membuatnya bagus untuk memvisualisasikan dataset dengan banyak fitur. Kita menggunakan voting.tab untuk memetakan data 16 dimensi ke grafik 2D. Lalu kita menggunakan Scatter Plot untuk memplot embeddings.		Widget Manifold Learning mengubah data dimensi tinggi menjadi pendekatan dimensi yang lebih rendah. Ini membuatnya bagus untuk memvisualisasikan dataset dengan banyak fitur. Kita menggunakan voting.tab untuk memetakan data 16 dimensi ke grafik 2D. Lalu kita menggunakan widget Scatter Plot untuk memplot embeddings.

	[[File:Manifold-learning-example.png\|center\|600px\|thumb]]		[[File:Manifold-learning-example.png\|center\|600px\|thumb]]

Onnowpurbo: /* Contoh */

2020-04-13T11:48:41Z

Contoh

← Older revision		Revision as of 11:48, 13 April 2020
Line 63:		Line 63:
	Widget Manifold Learning mengubah data dimensi tinggi menjadi pendekatan dimensi yang lebih rendah. Ini membuatnya bagus untuk memvisualisasikan dataset dengan banyak fitur. Kita menggunakan voting.tab untuk memetakan data 16 dimensi ke grafik 2D. Lalu kita menggunakan Scatter Plot untuk memplot embeddings.		Widget Manifold Learning mengubah data dimensi tinggi menjadi pendekatan dimensi yang lebih rendah. Ini membuatnya bagus untuk memvisualisasikan dataset dengan banyak fitur. Kita menggunakan voting.tab untuk memetakan data 16 dimensi ke grafik 2D. Lalu kita menggunakan Scatter Plot untuk memplot embeddings.

	[[File:Manifold-learning-example.png\|center\|~~200px~~\|thumb]]		[[File:Manifold-learning-example.png\|center\|600px\|thumb]]


	==Referensi==		==Referensi==

Onnowpurbo at 01:00, 10 March 2020

2020-03-10T01:00:43Z

← Older revision		Revision as of 01:00, 10 March 2020
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	~~Nonlinear dimensionality reduction~~.		Pengurangan / Reduksi dimensi secara nonlinear.

	==Input==		==Input==
Line 13:		Line 13:
	Transformed Data: dataset with reduced coordinates		Transformed Data: dataset with reduced coordinates

	Manifold Learning ~~is a technique which finds a~~ non-linear ~~manifold within the higher-dimensional space~~. ~~The widget then outputs new coordinates which correspond to a two-dimensional space~~. ~~Such data can be later visualized with~~ Scatter Plot ~~or other visualization widgets~~.		Manifold Learning adalah teknik yang menemukan manifold non-linear dalam ruang dimensi yang lebih tinggi. Widget kemudian menampilkan koordinat baru yang sesuai dengan ruang dua dimensi. Data tersebut dapat kemudian divisualisasikan dengan Scatter Plot atau widget visualisasi lainnya.

	[[File:Manifold-learning-stamped.png\|center\|200px\|thumb]]		[[File:Manifold-learning-stamped.png\|center\|200px\|thumb]]
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	* Produce a report.		* Produce a report.

	Manifold Learning ~~widget produces different~~ embeddings ~~for~~ high-dimensional ~~data~~.		Widget Manifold Learning menghasilkan embeddings yang berbeda untuk data high-dimensional.

	[[File:Collage-manifold.png\|center\|200px\|thumb]]		[[File:Collage-manifold.png\|center\|200px\|thumb]]

	~~From left to right~~, ~~top to bottom~~: t-SNE, MDS, Isomap, Locally Linear Embedding ~~and~~ Spectral Embedding.		Dari kiri ke kanan, atas ke bawah: t-SNE, MDS, Isomap, Locally Linear Embedding dan Spectral Embedding.

	==Contoh==		==Contoh==

	Manifold Learning ~~widget transforms high-dimensional~~ data ~~into a lower dimensional approximation~~. ~~This makes it great for visualizing datasets with many features~~. ~~We used~~ voting.tab ~~to map~~ 16~~-dimensional data onto a~~ 2D ~~graph~~. ~~Then we used~~ Scatter Plot ~~to plot the~~ embeddings.		Widget Manifold Learning mengubah data dimensi tinggi menjadi pendekatan dimensi yang lebih rendah. Ini membuatnya bagus untuk memvisualisasikan dataset dengan banyak fitur. Kita menggunakan voting.tab untuk memetakan data 16 dimensi ke grafik 2D. Lalu kita menggunakan Scatter Plot untuk memplot embeddings.

	[[File:Manifold-learning-example.png\|center\|200px\|thumb]]		[[File:Manifold-learning-example.png\|center\|200px\|thumb]]

Onnowpurbo at 01:54, 29 January 2020

2020-01-29T01:54:51Z

← Older revision		Revision as of 01:54, 29 January 2020
Line 5:		Line 5:
	Nonlinear dimensionality reduction.		Nonlinear dimensionality reduction.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset

	~~Outputs~~		==Output==

	Transformed Data: dataset with reduced coordinates		Transformed Data: dataset with reduced coordinates

	Manifold Learning is a technique which finds a non-linear manifold within the higher-dimensional space. The widget then outputs new coordinates which correspond to a two-dimensional space. Such data can be later visualized with Scatter Plot or other visualization widgets.		Manifold Learning is a technique which finds a non-linear manifold within the higher-dimensional space. The widget then outputs new coordinates which correspond to a two-dimensional space. Such data can be later visualized with Scatter Plot or other visualization widgets.
Line 18:		Line 18:


	Method for manifold learning:		* Method for manifold learning:
	t-SNE		** t-SNE
	MDS, see also MDS widget		** MDS, see also MDS widget
	Isomap		** Isomap
	Locally Linear Embedding		** Locally Linear Embedding
	Spectral Embedding		** Spectral Embedding
	Set parameters for the method:		* Set parameters for the method:
	t-SNE (distance measures):		** t-SNE (distance measures):
	Euclidean distance		*** Euclidean distance
	Manhattan		*** Manhattan
	Chebyshev		*** Chebyshev
	Jaccard		*** Jaccard
	Mahalanobis		*** Mahalanobis
	Cosine		*** Cosine
	MDS (iterations and initialization):		** MDS (iterations and initialization):
	max iterations: maximum number of optimization interactions		*** max iterations: maximum number of optimization interactions
	initialization: method for initialization of the algorithm (PCA or random)		*** initialization: method for initialization of the algorithm (PCA or random)
	Isomap:		** Isomap:
	number of neighbors		*** number of neighbors
	Locally Linear Embedding:		** Locally Linear Embedding:
	method:		*** method:
	standard		**** standard
	modified		**** modified
	hessian eigenmap		**** hessian eigenmap
	local		**** local
	number of neighbors		*** number of neighbors
	max iterations		*** max iterations
	Spectral Embedding:		** Spectral Embedding:
	affinity:		*** affinity:
	nearest neighbors		**** nearest neighbors
	RFB kernel		**** RFB kernel
	Output: the number of reduced features (components).		* Output: the number of reduced features (components).
	If Apply automatically is ticked, changes will be propagated automatically. Alternatively, click Apply.		* If Apply automatically is ticked, changes will be propagated automatically. Alternatively, click Apply.
	Produce a report.		* Produce a report.

	Manifold Learning widget produces different embeddings for high-dimensional data.		Manifold Learning widget produces different embeddings for high-dimensional data.

Onnowpurbo at 02:22, 24 January 2020

2020-01-24T02:22:15Z

← Older revision		Revision as of 02:22, 24 January 2020
Line 15:		Line 15:
	Manifold Learning is a technique which finds a non-linear manifold within the higher-dimensional space. The widget then outputs new coordinates which correspond to a two-dimensional space. Such data can be later visualized with Scatter Plot or other visualization widgets.		Manifold Learning is a technique which finds a non-linear manifold within the higher-dimensional space. The widget then outputs new coordinates which correspond to a two-dimensional space. Such data can be later visualized with Scatter Plot or other visualization widgets.

	~~../../_images/manifold~~-learning-stamped.png		[[File:Manifold-learning-stamped.png\|center\|200px\|thumb]]


	Method for manifold learning:		Method for manifold learning:

	t-SNE		t-SNE

	MDS, see also MDS widget		MDS, see also MDS widget

	Isomap		Isomap

	Locally Linear Embedding		Locally Linear Embedding

	Spectral Embedding		Spectral Embedding

	Set parameters for the method:		Set parameters for the method:

	t-SNE (distance measures):		t-SNE (distance measures):

	Euclidean distance		Euclidean distance

	Manhattan		Manhattan

	Chebyshev		Chebyshev

	Jaccard		Jaccard

	Mahalanobis		Mahalanobis

	Cosine		Cosine

	MDS (iterations and initialization):		MDS (iterations and initialization):

	max iterations: maximum number of optimization interactions		max iterations: maximum number of optimization interactions

	initialization: method for initialization of the algorithm (PCA or random)		initialization: method for initialization of the algorithm (PCA or random)

	Isomap:		Isomap:

	number of neighbors		number of neighbors

	Locally Linear Embedding:		Locally Linear Embedding:

	method:		method:

	standard		standard

	modified		modified

	hessian eigenmap		hessian eigenmap

	local		local

	number of neighbors		number of neighbors

	max iterations		max iterations

	Spectral Embedding:		Spectral Embedding:

	affinity:		affinity:

	nearest neighbors		nearest neighbors

	RFB kernel		RFB kernel

	Output: the number of reduced features (components).		Output: the number of reduced features (components).

	If Apply automatically is ticked, changes will be propagated automatically. Alternatively, click Apply.		If Apply automatically is ticked, changes will be propagated automatically. Alternatively, click Apply.

	Produce a report.		Produce a report.

	Manifold Learning widget produces different embeddings for high-dimensional data.		Manifold Learning widget produces different embeddings for high-dimensional data.

	~~../../_images/collage~~-manifold.png		[[File:Collage-manifold.png\|center\|200px\|thumb]]

	From left to right, top to bottom: t-SNE, MDS, Isomap, Locally Linear Embedding and Spectral Embedding.		From left to right, top to bottom: t-SNE, MDS, Isomap, Locally Linear Embedding and Spectral Embedding.
	~~Example~~
			==Contoh==

	Manifold Learning widget transforms high-dimensional data into a lower dimensional approximation. This makes it great for visualizing datasets with many features. We used voting.tab to map 16-dimensional data onto a 2D graph. Then we used Scatter Plot to plot the embeddings.		Manifold Learning widget transforms high-dimensional data into a lower dimensional approximation. This makes it great for visualizing datasets with many features. We used voting.tab to map 16-dimensional data onto a 2D graph. Then we used Scatter Plot to plot the embeddings.

	~~../../_images/manifold~~-learning-example.png		[[File:Manifold-learning-example.png\|center\|200px\|thumb]]

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/manifoldlearning.html Nonlinear dimensionality reduction. Inputs Data: input dataset Output..."

2020-01-17T02:02:39Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/manifoldlearning.html Nonlinear dimensionality reduction. Inputs Data: input dataset Output..."

New page

Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/manifoldlearning.html

Nonlinear dimensionality reduction.

Inputs

Data: input dataset

Outputs

Transformed Data: dataset with reduced coordinates

Manifold Learning is a technique which finds a non-linear manifold within the higher-dimensional space. The widget then outputs new coordinates which correspond to a two-dimensional space. Such data can be later visualized with Scatter Plot or other visualization widgets.

../../_images/manifold-learning-stamped.png

Method for manifold learning:

t-SNE

MDS, see also MDS widget

Isomap

Locally Linear Embedding

Spectral Embedding

Set parameters for the method:

t-SNE (distance measures):

Euclidean distance

Manhattan

Chebyshev

Jaccard

Mahalanobis

Cosine

MDS (iterations and initialization):

max iterations: maximum number of optimization interactions

initialization: method for initialization of the algorithm (PCA or random)

Isomap:

number of neighbors

Locally Linear Embedding:

method:

standard

modified

hessian eigenmap

local

number of neighbors

max iterations

Spectral Embedding:

affinity:

nearest neighbors

RFB kernel

Output: the number of reduced features (components).

If Apply automatically is ticked, changes will be propagated automatically. Alternatively, click Apply.

Produce a report.

Manifold Learning widget produces different embeddings for high-dimensional data.

../../_images/collage-manifold.png

From left to right, top to bottom: t-SNE, MDS, Isomap, Locally Linear Embedding and Spectral Embedding.
Example

Manifold Learning widget transforms high-dimensional data into a lower dimensional approximation. This makes it great for visualizing datasets with many features. We used voting.tab to map 16-dimensional data onto a 2D graph. Then we used Scatter Plot to plot the embeddings.

../../_images/manifold-learning-example.png

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/unsupervised/manifoldlearning.html

==Pranala Menarik==

* [[Orange]]