NDLI: Discriminative cluster analysis

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Using inaccurate models in reinforcement learning

Algorithms for portfolio management based on the Newton method

Higher order learning with graphs

Ranking on graph data

Robust probabilistic projections

A DC-programming algorithm for kernel selection

Relational temporal difference learning

A new approach to data driven clustering

Agnostic active learning

On a theory of learning with similarity functions

On Bayesian bounds

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An empirical comparison of supervised learning algorithms

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A continuation method for semi-supervised SVMs

A regularization framework for multiple-instance learning

Trading convexity for scalability

Learning algorithms for online principal-agent problems (and selling goods online)

Dealing with non-stationary environments using context detection

Locally adaptive classification piloted by uncertainty

The relationship between Precision-Recall and ROC curves

Discriminative cluster analysis

Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations

Learning the structure of Factored Markov Decision Processes in reinforcement learning problems

Efficient learning of Naive Bayes classifiers under class-conditional classification noise

Learning user preferences for sets of objects

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A graphical model for predicting protein molecular function

Qualitative reinforcement learning

Online multiclass learning by interclass hypothesis sharing

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An analysis of graph cut size for transductive learning

Learning a kernel function for classification with small training samples

Looping suffix tree-based inference of partially observable hidden state

Batch mode active learning and its application to medical image classification

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Hidden process models

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Automatic basis function construction for approximate dynamic programming and reinforcement learning

Personalized handwriting recognition via biased regularization

Optimal kernel selection in Kernel Fisher discriminant analysis

Pareto optimal linear classification

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Autonomous shaping: knowledge transfer in reinforcement learning

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Efficient MAP approximation for dense energy functions

Nonstationary kernel combination

Region-based value iteration for partially observable Markov decision processes

Multiclass boosting with repartitioning

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Spectral clustering for multi-type relational data

Combined central and subspace clustering for computer vision applications

Fast direct policy evaluation using multiscale analysis of Markov diffusion processes

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The uniqueness of a good optimum for K-means

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Online decoding of Markov models under latency constraints

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Reinforcement learning for optimized trade execution

Concept boundary detection for speeding up SVMs

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Categorization in multiple category systems

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Sequential update of ADtrees

Predictive linear-Gaussian models of controlled stochastic dynamical systems

A statistical approach to rule learning

Efficient inference on sequence segmentation models

Cost-sensitive learning with conditional Markov networks

Feature value acquisition in testing: a sequential batch test algorithm

Permutation invariant SVMs

Bayesian learning of measurement and structural models

An intrinsic reward mechanism for efficient exploration

Deterministic annealing for semi-supervised kernel machines

Feature subset selection bias for classification learning

Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features

An investigation of computational and informational limits in Gaussian mixture clustering

Bayesian pattern ranking for move prediction in the game of Go

PAC model-free reinforcement learning

Experience-efficient learning in associative bandit problems

Full Bayesian network classifiers

Local Fisher discriminant analysis for supervised dimensionality reduction

Iterative RELIEF for feature weighting

Multiclass reduced-set support vector machines

Fast and space efficient string kernels using suffix arrays

Bayesian regression with input noise for high dimensional data

Probabilistic inference for solving discrete and continuous state Markov Decision Processes

Clustering graphs by weighted substructure mining

Active sampling for detecting irrelevant features

Accelerated training of conditional random fields with stochastic gradient methods

Topic modeling: beyond bag-of-words

Label propagation through linear neighborhoods

Two-dimensional solution path for support vector regression

Totally corrective boosting algorithms that maximize the margin

Inference with the Universum

Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems

Predictive state representations with options

Fast time series classification using numerosity reduction

A duality view of spectral methods for dimensionality reduction

Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture

Discriminative unsupervised learning of structured predictors

Semi-supervised nonlinear dimensionality reduction

Null space versus orthogonal linear discriminant analysis

Active learning via transductive experimental design

Collaborative ordinal regression

Block-quantized kernel matrix for fast spectral embedding

Statistical debugging: simultaneous identification of multiple bugs

Efficient lazy elimination for averaged one-dependence estimators

Discriminative cluster analysis

Content Provider	ACM Digital Library
Author	Kanade, Takeo De la Torre, Fernando
Abstract	Clustering is one of the most widely used statistical tools for data analysis. Among all existing clustering techniques, k-means is a very popular method because of its ease of programming and because it accomplishes a good trade-off between achieved performance and computational complexity. However, k-means is prone to local minima problems, and it does not scale too well with high dimensional data sets. A common approach to dealing with high dimensional data is to cluster in the space spanned by the principal components (PC). In this paper, we show the benefits of clustering in a low dimensional discriminative space rather than in the PC space (generative). In particular, we propose a new clustering algorithm called Discriminative Cluster Analysis (DCA). DCA jointly performs dimensionality reduction and clustering. Several toy and real examples show the benefits of DCA versus traditional PCA+k-means clustering. Additionally, a new matrix formulation is proposed and connections with related techniques such as spectral graph methods and linear discriminant analysis are provided.
Starting Page	241
Ending Page	248
Page Count	8
File Format	PDF
ISBN	1595933832
DOI	10.1145/1143844.1143875
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-06-25
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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