There are 12 repositories under principal-component-analysis topic.
Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
:crown: Multivariate exploratory data analysis in Python — PCA, CA, MCA, MFA, FAMD, GPA
Fast Best-Subset Selection Library
quizzes/assignments for mathematics for machine learning specialization on coursera
Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.
The foundational library of the Morpheus data science framework
Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
This research project will illustrate the use of machine learning and deep learning for predictive analysis in industry 4.0.
✍️ An intelligent system that takes a document and classifies different writing styles within the document using stylometric techniques.
An approach to document exploration using Machine Learning. Let's cluster similar research articles together to make it easier for health professionals and researchers to find relevant research articles.
A MATLAB toolbox for classifier: Version 1.0.7
Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model
Explorative multivariate statistics in Python
The goal of this project is to solve the task of name transcription from handwriting images implementing a NN approach.
Synthesis of individualized HRTFs based on Neural Networks, Principal Component Analysis and anthropometry
Implementation of Machine Learning Algorithms
This repository contains lecture notes and codes for the course "Computational Methods for Data Science"
implement the machine learning algorithms by python for studying
A sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).
Implementation of PCA/2D-PCA/2D(Square)-PCA in Python for recognizing Faces: 1. Single Person Image 2. Group Image 3. Recognize Face In Video
Machine learning is widely used in bioinformatics and particularly in breast cancer diagnosis. In this project, certain classification methods such as K-nearest neighbors (K-NN) and Support Vector Machine (SVM) which is a supervised learning method to detect breast cancer are used.
Misc Statistics and Machine Learning codes in R
Information Retrieval in High Dimensional Data (class deliverables)