Dimensionality reduction

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Related lectures (32)

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Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Clustering & Density Estimation

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Dimensionality Reduction

Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.

KNN: Nearest Neighbor Classifier

Covers the k-Nearest-Neighbor classifier, hand-written digit recognition, multi-class k-NN, data reduction, applications, graph construction, limitations, and the curse of dimensionality.

Dimensionality Reduction: Introduction and Motivation

Covers the challenges of high-dimensional data, the need for dimensionality reduction, and the implications for machine learning models.

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

Nearest Neighbor Search: Johnson-Lindenstrauss Lemma

Covers the Nearest Neighbor search algorithm and the Johnson-Lindenstrauss lemma for dimensionality reduction, exploring preprocessing techniques and locality-sensitive hashing.

Principal Component Analysis: Geometric Interpretation and Dimension Reduction

Explores Principal Component Analysis for dimension reduction and data representation in a new basis.

Kernel PCA: Nonlinear Dimensionality Reduction

Explores Kernel Principal Component Analysis, a nonlinear method using kernels for linear problem solving and dimensionality reduction.

PCA: Key Concepts

Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.