Different Features
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Different Features Different Features
description
Different Features. Glasses vs. No Glasses. Beard vs. No Beard. Beard Distinction. Ghodsi et, al 2007. Glasses Distinction. Ghodsi et, al 2007. Multiple-Attribute Metric. Ghodsi et, al 2007. Embedding of sparse music similarity graph. Platt, 2004. Reinforcement learning. - PowerPoint PPT Presentation
Transcript of Different Features
Different FeaturesDifferent Features
Glasses vs. No GlassesGlasses vs. No Glasses
Beard vs. No BeardBeard vs. No Beard
Beard DistinctionBeard Distinction
Ghodsi et, al 2007
Glasses DistinctionGlasses Distinction
Ghodsi et, al 2007
Multiple-Attribute MetricMultiple-Attribute Metric
Ghodsi et, al 2007
Embedding of sparse music Embedding of sparse music similarity graphsimilarity graph
Platt, 2004
Reinforcement learningReinforcement learning
Mahadevan and Maggioini, 2005
Semi-supervised learningSemi-supervised learning
Use graph-based discretization of manifold to infer missing labels.
Build classifiers from bottom eigenvectors of graph Laplacian.
Belkin & Niyogi, 2004; Zien et al, Eds., 2005
correspondencescorrespondences
http://www.bushorchimp.com
Learning correspondencesLearning correspondences
How can we learn manifold structure that is shared across multiple data sets?
c et al, 2003, 2005
Mapping and robot localizationMapping and robot localization
Bowling, Ghodsi, Wilkinson 2005
Ham, Lin, D.D. 2005
ClassificationClassification
ClassificationClassification
DataData
Features (X)
(Green, 6, 4, 4.5)
(Green, 7, 4.5, 5)
(Red, 6, 3, 3.5)
(Red, 4.5, 4, 4.5)
(Yellow, 1.5, 8, 2)
(Yellow, 1.5, 7, 2.5)
Data RepresentationData Representation
Data RepresentationData Representation
11 11 11 11 11
11 00 11 00 11
11 11 11 11 11
11 0.50.5 0.50.5 0.50.5 11
11 11 11 11 11
Data RepresentationData Representation
Features and labelsFeatures and labels
(Green, 6, 4, 4.5)
(Green, 7, 4.5, 5)
(Red, 6, 3, 3.5)
(Red, 4.5, 4, 4.5)
(Yellow, 1.5, 8, 2)
(Yellow, 1.5, 7, 2.5)
Green Pepper
Green Pepper
Red Pepper
Red Pepper
Hot Pepper
Hot Pepper
Features and labelsFeatures and labels
Objects Features (X) Labels (Y)
Classification (New point)Classification (New point)
(Red, 7, 4, 4.5)h(Red, 7, 4, 4.5)
?
Classification (New point)Classification (New point)
(Red, 5, 3, 4.5)h(Red, 5, 3, 4.5)
?
Digit RecognitionDigit Recognition
ClassificationClassification
ClassificationClassification
ClassificationClassification
ClassificationClassification
Computer VisionComputer Vision
N. Jojic and B.J. Frey, “ Learning flexible sprites in video layers”, CVPR 2001, (Video)
ReadingReading
• Journals: Neural Computation, JMLR, ML, IEEE PAMI• Conferences: NIPS, UAI, ICML, AI-STATS, IJCAI,
IJCNN• Vision: CVPR, ECCV, SIGGRAPH• Speech: EuroSpeech, ICSLP, ICASSP• Online: citesser, google• Books:
– Elements of Statistical Learning, Hastie, Tibshirani, Friedman– Learning from Data, Cherkassky, Mulier– Pattern classification, Duda, Hart, Stork– Neural Networks for pattern Recognition, Bishop– Pattern recognition and machine learning, Bishop
