Local Linear Embedding (LLE) - Revision history

BPeat at 02:59, 6 March 2024

2024-03-06T02:59:33Z

BPeat at 13:49, 13 September 2023

2023-09-13T13:49:35Z

BPeat at 00:13, 17 August 2023

2023-08-17T00:13:21Z

BPeat at 06:21, 11 July 2023

2023-07-11T06:21:46Z

BPeat: Text replacement - "http://" to "https://"

2023-03-29T02:05:52Z

Text replacement - "http://" to "https://"

BPeat at 13:34, 5 April 2020

2020-04-05T13:34:01Z

BPeat at 13:27, 5 April 2020

2020-04-05T13:27:22Z

BPeat at 13:03, 5 April 2020

2020-04-05T13:03:20Z

BPeat at 12:57, 5 April 2020

2020-04-05T12:57:47Z

BPeat at 12:55, 5 April 2020

2020-04-05T12:55:10Z

@@ Line 8: / Line 8: @@
 [https://www.google.com/search?q=Local+Linear+Embedding+machine+learning+ML ...Google search]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 * [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]

@@ Line 9: / Line 9: @@
 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
-* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]
 ** [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]]

@@ Line 9: / Line 9: @@
 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
-* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]
 ** [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]]

@@ Line 8: / Line 8: @@
 [https://www.google.com/search?q=Local+Linear+Embedding+machine+learning+ML ...Google search]
-* [[AI Solver]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
-* [[...find outliers]]
+* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
-* [[Embedding]]
+* [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]
 ** [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]]
 * [[Dimensional Reduction]]
 * [[Isomap]]
-* [[Kernel Trick]]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
 * [https://cs.nyu.edu/~roweis/lle/ Locally Linear Embedding | S.T. Roweis & L. K. Saul - NYU]
 * [https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction Nonlinear  dimensionality reduction | Wikipedia]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Local+Linear+Embedding YouTube search...]
+[https://www.youtube.com/results?search_query=Local+Linear+Embedding YouTube search...]
-[http://www.google.com/search?q=Local+Linear+Embedding+machine+learning+ML ...Google search]
+[https://www.google.com/search?q=Local+Linear+Embedding+machine+learning+ML ...Google search]
 * [[AI Solver]]
@@ Line 17: / Line 17: @@
 * [[Isomap]]
 * [[Kernel Trick]]
-* [http://cs.nyu.edu/~roweis/lle/ Locally Linear Embedding | S.T. Roweis & L. K. Saul - NYU]
+* [https://cs.nyu.edu/~roweis/lle/ Locally Linear Embedding | S.T. Roweis & L. K. Saul - NYU]
-* [http://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction Nonlinear  dimensionality reduction | Wikipedia]
+* [https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction Nonlinear  dimensionality reduction | Wikipedia]
-begins by finding a set of the nearest neighbors of each point. It then computes a set of weights for each point that best describes the point as a linear combination of its neighbors. Finally, it uses an [http://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors]-based optimization technique to find the low-dimensional embedding of points, such that each point is still described with the same linear combination of its neighbors. LLE tends to handle non-uniform sample densities poorly because there is no fixed unit to prevent the weights from drifting as various regions differ in sample densities. LLE has no internal model. LLE was presented at approximately the same time as Isomap. It has several advantages over Isomap, including faster optimization when implemented to take advantage of sparse matrix algorithms, and better results with many problems
+begins by finding a set of the nearest neighbors of each point. It then computes a set of weights for each point that best describes the point as a linear combination of its neighbors. Finally, it uses an [https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors]-based optimization technique to find the low-dimensional embedding of points, such that each point is still described with the same linear combination of its neighbors. LLE tends to handle non-uniform sample densities poorly because there is no fixed unit to prevent the weights from drifting as various regions differ in sample densities. LLE has no internal model. LLE was presented at approximately the same time as Isomap. It has several advantages over Isomap, including faster optimization when implemented to take advantage of sparse matrix algorithms, and better results with many problems
-http://www.researchgate.net/publication/282773146/figure/fig2/AS:317438165045261@1452694564973/Steps-of-locally-linear-embedding-algorithm.png
+https://www.researchgate.net/publication/282773146/figure/fig2/AS:317438165045261@1452694564973/Steps-of-locally-linear-embedding-algorithm.png

@@ Line 11: / Line 11: @@
 * [[...find outliers]]
 * [[Embedding]]
 * [[Anomaly Detection]]
 * [[Dimensional Reduction]]
 * [[Principal Component Analysis (PCA)]]
 * [[Isomap]]
 * [[Kernel Trick]]
@@ Line 20: / Line 20: @@
 * [http://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction Nonlinear  dimensionality reduction | Wikipedia]
-Embedding...
+Embeddings...
 * projecting an input into another more convenient representation space. For example we can project (embed) faces into a space in which face matching can be more reliable. | [http://www.quora.com/profile/Chomba-Bupe Chomba Bupe]
 * a mapping of a discrete — categorical — variable to a vector of continuous numbers. In the context of neural networks, embeddings are low-dimensional, learned continuous vector representations of discrete variables. Neural network embeddings are useful because they can reduce the dimensionality of categorical variables and meaningfully represent categories in the transformed space. [http://towardsdatascience.com/neural-network-embeddings-explained-4d028e6f0526 Neural Network Embeddings Explained | Will Koehrsen - Towards Data Science]
 Neural network embeddings have 3 primary purposes:

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