ABC
1
FilenameDemos featured in Description
2
ml_unsupervised_clusterKmeans.mdemo_unsupervised_clusteringCluster observations in model matrix X using K-means algorithm with optional K-means++ initialization
3
ml_unsupervised_DBcluster.mdemo_unsupervised_clusteringCluster observations in model matrix X using density-based clustering algorithm
4
ml_unsupervised_dimRedFA.mdemo_unsupervised_PCA_FactorAnalysisReduce dimensionality of model matrix X using Factor Analysis
5
ml_unsupervised_dimRedKPCA.mdemo_unsupervised_ISOMAP, demo_unsupervised_kernelPCAUse kernalized PCA to reduce dimensionality of model matrix X
6
ml_unsupervised_dimRedPCA.mdemo_unsupervised_PCA_FactorAnalysisUse PCA to find best low-rank approximation of model matrix X where best is minimizing error in Frobenius norm
7
ml_unsupervised_HMM.mdemo_unsupervised_HMMModels observed data in input X as generated by a hidden state with a chain structured having the Markov property
8
ml_unsupervised_LGSSM.mdemo_unsupervised_KalmanTrackingModels observed data X as generated by a Gaussian distribution whose parameters depend upon a latent variable distribution, also Gaussian distributed, which changes over time according to a linear combination of previous states
9
ml_visualize_ISOMAP.mdemo_unsupervised_ISOMAPApply multidimensional scaling using geodeisc distance defined over nearest neighbours graph to learn manifold and visualize in lower dimensions
10
ml_visualize_MDS.mdemo_unsupervised_MDS_SammonApply MDS with Euclidean distance function
11
ml_visualize_Sammon.mdemo_unsupervised_MDS_SammonApply MDS with Sammon's distance function
12
ml_visualize_tSNE.mdemo_unsupervised_tSNEFind low-dimensional representation of model matrix X by minimizing KL-divergence between probability distributions representing similarity of points in high-dimensional space and similarity of points in low-dimensional points
13
ml_unsupervised_sparseAutoencoder.mdemo_unsupervised_sparseAutoencoderFit a feed-forward neural network with linear neurons using sigmoid activiation function, using reconstruction error (Frobenius norm) as loss function.
14
ml_unsupervised_HMM.mdemo_unsupervised_HMM.mModels observation data in input X as generated by a chain-structured latent variable distribution with the Markov property
15
ml_unsupervised_LGSSM.mdemo_unsupervised_KalmanTracking.mImplements Kalman filtering and Kalman smoothing for directed probabilistic models that have Gaussian latent variables with a Markov transition structure and Gaussian observation structure.