Search results
Unsupervised from unlabeled. approach for learning a lower-dimensional feature training data. Originally: Linear + nonlinearity (sigmoid) Later: Deep, Features.
Dec 16, 2020 · Unsupervised representation learning enables state-of-the-art supervised prediction of mutational effect and secondary structure and improves state-of-the-art features for long-range contact prediction. Abstract.
- Machine Learning Methods
- Unsupervised Learning
- X’s
- Challenges of Unsupervised Learning
- Types of Unsupervised Learning
- Clustering
- Types of Clustering
- K-means Clustering
- W(Ck)
- Kn
- K-means Clustering Algorithm
- Cons:
- Building a Dendrogram
- Distance Between Groups
- Linkage:
- Types of Linkage
- Pros:
- Cons:
- Dimensionality Reduction
- Principal Component Analysis
- Principal Component Analysis
- Principal Components
Yes No Category Quantity Yes No Do you have labeled data? Supervised What do you want to predict? Unsupervised Do you want to group the data? Classification Regression Clustering Dimensionality reduction KNN Logistic Regression SVM CART Linear Regression Ridge Regression Lasso K-means Hierarchical PCA
Recall: A set of statistical tools for data that only has features/input available, but no response. In other words, we have
but no labels y. Goal: Discover interesting patterns/properties of the data. • E.g. for visualizing or interpreting high-dimensional data. Unsupervised Learning Example applications: Given tissue samples from n patients with breast cancer, identify unknown subtypes of breast cancer. Gene expression experiments have thousands of variables. Repres...
Why is unsupervised learning challenging? Exploratory data analysis — goal is not always clearly defined Difficult to assess performance — “right answer” unknown Working with high-dimensional data
Two approaches: Cluster analysis For identifying homogenous subgroups of samples Dimensionality reduction For finding a low-dimensional representation to characterize and visualize the data Cluster Analysis
Cluster A Cluster B Cluster C Dataset Cluster D Clustering http://cs.joensuu.fi/sipu/datasets/
Centroid-based clustering Hierarchical clustering Model-based clustering Each cluster is represented by a parametric distribution Dataset is a mixture of distributions Hard vs. soft/fuzzy clustering Hard: observations divided into distinct clusters Soft: observations may belong to more than one cluster
Groups data into K clusters that satisfy two properties. Each observation belongs to at least one of the K clusters. Clusters are non-overlapping. No observation belongs to more than one cluster. K-means Clustering A good clustering is one for which the within-cluster variation is as small as possible. Denote each cluster by
be a measure of the within-cluster variation. K-means aims to solve K-means Clustering How to measure within-cluster variation? The most common choice is squared Euclidean distance. Which means overall we solve K-means Clustering It turns out that this optimization problem is difficult to solve, as it is discrete and there are nearly
ways to split n samples into K clusters. In practice, use an iterative algorithm that finds a local minimum to this optimization.
Initialize each observation to a cluster by randomly assigning a cluster, from 1 to K, to each observation. Iterate until the cluster assignments stop changing: For each of the K clusters, compute the cluster centroid. The k-th cluster centroid is the vector of the p feature means for the observations in the k-th cluster. Assign each observati...
Not robust to data perturbations and different initializations Treats each feature equally, not robust to noise features or different scales of features — looks for in spherical clusters in feature space Need to define K before running algorithm Another K-means Example
A dendrogram is most commonly built using a bottom-up or agglomerative algorithm. We start at the leaves and group observations until we reach the root containing the entire dataset. Like in k-means, we need a measure of similarity. Again, the most common is Euclidean distance.
It’s easy to compute Euclidean distance between two observations. What is the distance or similarity between two groups or clusters of observations?
defines the dissimilarity between two groups of observations. Most common types are complete, average, single, and centroid.
Complete linkage Single linkage Average linkage Centroid linkage
• Don’t have to choose a value of K (number of clusters) before running algorithm
Do have to pick where to cut the dendrogram to obtain clusters Sensitive to similarity measure and type of linkage used
Dimensionality Reduction Recall the curse of dimensionality when working in high dimensions. Dimensionality reduction is the process of reducing the number of features under consideration. We already saw some examples of this in the lasso and forward/ backward selection algorithms. These methods reduce dimensionality by selecting a subset of featur...
Look for a low-dimensional representation of the dataset that contains as much variation in the dataset as possible. E.g. for plotting our data and gaining intuition, if we can obtain a 2D representation of the data, then we can plot the observations in this low-dimensional space. Note that you want to center the data and make the scales of feature...
First two principal axes of this Gaussian dataset. Principal Component Analysis
Equivalently, find eigenvectors with the largest eigenvalues of the sample covariance matrix. By the singular value decomposition (SVD), Principal Components Equivalently, find eigenvectors with the largest eigenvalues of the sample covariance matrix. By the singular value decomposition (SVD), The right singular vectors are the loadings, or princip...
Apr 30, 2024 · Unsupervised Learning. In previous chapters, we have largely focused on classication and regression problems, where we use supervised learning with training samples that have both features/inputs and corresponding outputs or labels, to learn hypotheses or models that can then be used to predict labels for new data.
Within artificial intelligence (AI) and machine learning, there are two basic approaches: supervised learning and unsupervised learning. The main difference is that one uses labeled data to help predict outcomes, while the other does not.
Jun 29, 2023 · Supervised and unsupervised learning represent two distinct approaches in the field of machine learning, with the presence or absence of labeling being a defining factor. Supervised learning harnesses the power of labeled data to train models that can make accurate predictions or classifications.
People also ask
What is the difference between supervised and unsupervised learning?
What is unsupervised learning in data science?
What is unsupervised representation learning?
What is unsupervised learning in machine learning?
Unsupervised learning has numerous applications: Visualization: identifying and making accessible useful hidden structure in the data. Anomaly detection: identifying factory components that are likely to break soon.
