Orange: Manifold Learning - Revision history

Onnowpurbo: /* Contoh */

2020-04-15T03:55:27Z

Contoh

Onnowpurbo: /* Output */

2020-04-15T01:45:00Z

Output

Onnowpurbo at 21:53, 13 April 2020

2020-04-13T21:53:16Z

Onnowpurbo: /* Contoh */

2020-04-13T11:48:41Z

Contoh

Onnowpurbo at 01:00, 10 March 2020

2020-03-10T01:00:43Z

Onnowpurbo at 01:54, 29 January 2020

2020-01-29T01:54:51Z

Onnowpurbo at 02:22, 24 January 2020

2020-01-24T02:22:15Z

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]]

@@ Line 59: / Line 59: @@
 ==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]]

@@ Line 53: / Line 53: @@
 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.

@@ Line 1: / Line 1: @@
 Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/manifoldlearning.html
+Widget Manifold Learning dapat melakukan pengurangan / reduksi dimensi secara nonlinear.
 ==Input==
@@ Line 13: / Line 11: @@
   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]]
@@ Line 61: / Line 59: @@
 ==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]]

@@ 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.
-[[File:Manifold-learning-example.png|center|200px|thumb]]
+[[File:Manifold-learning-example.png|center|600px|thumb]]
 ==Referensi==

@@ Line 3: / Line 3: @@
-Nonlinear dimensionality reduction.
+Pengurangan / Reduksi dimensi secara nonlinear.
 ==Input==
@@ Line 13: / Line 13: @@
   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]]
@@ Line 53: / Line 53: @@
 * 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]]
-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==
-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]]

@@ Line 5: / Line 5: @@
 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.
@@ 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.