semi-supervised learning with graphs › ~zhuxj › tmp › thesis_defense.pdf · 2005-06-09 ·...
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Semi-Supervised Learning with Graphs
Xiaojin (Jerry) Zhu
LTI · SCS · CMU
Thesis Committee
John Lafferty (co-chair)
Ronald Rosenfeld (co-chair)
Zoubin Ghahramani
Tommi Jaakkola
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Semi-supervised Learning
• classifiers need labeled data to train
• labeled data scarce, unlabeled data abundant
• Traditional classifiers cannot train on unlabeled data.
My thesis (semi-supervised learning): Develop classification
methods that can use both labeled and unlabeled data.
2
Motivating example 1: speech recognition
• sounds −→ words
• label data: transcription 10 to 400 real-time
• unlabeled data: radio, call center. Useful?
3
Motivating example 2: parsing
• sentences −→ parse trees
NP VP
PN VB NP PP
DET N P NP
DET N
S
I saw a falcon with a telescope
• labeled data: treebank (Hwa, 2003):
I English 40,000 sentences, 5 years; Chinese 4,000 sentences, 2 years
• unlabeled data: sentences without annotation. Useful?
4
Motivating example 3: video surveillance
• images −→ identities
• labeled data: only a few images
• unlabeled data: abundant. Useful?
5
The message
Unlabeled data can improve classification.
6
Why unlabeled data might help
example: classify astronomy vs. travel articles
d1 d3 d4 d2
asteroid • •bright • •comet •
yearzodiac
...airport
bikecamp •
yellowstone • •zion •
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Why labeled data alone might fail
d1 d3 d4 d2
asteroid •bright •comet
yearzodiac •
...airport •
bike •camp
yellowstone •zion •
• no overlap!
• tends to happen when labeled data is scarce
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Unlabeled data are stepping stones
d1 d5 d6 d7 d3 d4 d8 d9 d2
asteroid •bright • •comet • •
year • •zodiac • •
...airport •
bike • •camp • •
yellowstone • •zion •
• observe direct similarity d1 ∼ d5, d5 ∼ d6, . . .
• assume similar features ⇒ same label
• infer indirect similarity
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Unlabeled data are stepping stones
• arrange l labeled and u unlabeled(=test) points in a graph
I nodes: the n = l + u points
I weighted edges W : direct similarity, e.g. Wij = exp(−‖xi−xj‖2
2σ2
)• infer indirect similarity (using all paths)
d1
d2
d4
d3
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One way to use labeled and unlabeled data(Zhu and Ghahramani, 2002)
• input: n× n graph weights W (important!)
labels Yl ∈ 0, 1l
• create transition matrix Pij = Wij/∑
j′Wij′
• start from any n-vector f , repeat:
I clamp labeled data fl = Yl
I propagate f ← Pf
• f converges to a unique solution, the harmonic function.
0 ≤ f ≤ 1, “soft labels”
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An electric network interpretation(Zhu, Ghahramani and Lafferty, ICML2003)
• harmonic function f is the voltage at the nodes
I edges are resistors with R = 1/WI 1 volt battery connects to labeled nodes
+1 volt
wijR =ij
1
1
0
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A random walk interpretation
harmonic function fi = P (hit label 1 | start from i)
demo
13
Closed form solution for the harmonic function
• diagonal degree matrix D: Dii =∑
j Wij
• graph Laplacian matrix ∆ = D −W
fu = −∆uu−1∆ulYl
• ∆: graph version of the continuous Laplacian operator
∇2 = ∂2
∂x2 + ∂2
∂y2 + ∂2
∂z2
• harmonic: ∆f = 0 on unlabeled data, with Dirichlet boundary
conditions on labeled data
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Properties of the harmonic function
• currents in-flow = out-flow at any node (Kirchoff’s law)
• min energy E(f) =∑
i∼j Wij(fi − fj)2 = f>∆f
• average of neighbors: fu(i) =P
j∼i Wijf(j)Pj∼i Wij
• uniquely exists
• 0 ≤ f ≤ 1
15
Harmonic functions for text categorization
accuracy
baseball PC religion
hockey Mac atheism
50 labeled articles, about 2000 unlabeled articles. 10NN graph.
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Harmonic functions for digit recognition
accuracy
1 vs. 2 10 digits
50 labeled images, about 4000 unlabeled images, 10NN graph
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Why it works on text? Good graphs
• cosine on tf.idf
• quotes
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Why it works on digits? Good graphs
• pixel-wise Euclidean distance
not similar indirectly similar
with stepping stones
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Where do good graphs come from?
• domain knowledge
• learn hyperparameters Wij = exp(−
∑d
(xid−xjd)2
αd
)with
e.g. Bayesian evidence maximization
100
150
200
250
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150
200
250
126
128
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136
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average 7 average 9 learned α
good graphs + harmonic functions = semi-supervised learning
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Too much unlabeled data?
• does it scale?
I fu = −∆uu−1∆ulYl
I O(u3), e.g. millions of crawled web pages
• solution 1: use iterative methods
I the label propagation algorithm (slow)
I loopy belief propagation
I conjugate gradient
• solution 2: reduce problem size
I use a random small unlabeled subset (Delalleau et al. 2005)
I harmonic mixtures
• can it handle new points (induction)?
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Harmonic mixtures(Zhu and Lafferty, 2005)
• fit unlabeled data with a mixture model, e.g.
I Gaussian mixtures for images
I multinomial mixtures for documents
• use EM or other methods
• M mixture components, here
M = 30 u ≈ 1000
• learn soft labels for the mixture
components, not the unlabeled
points
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Harmonic mixtureslearn labels for mixture components
• assume mixture component labels λ1, · · · , λM
• λ determines f
I mixture model responsibility R: Rim = p(m|xi)
I f(i) =∑M
m=1Rimλm
• learn λ such that f is closest to harmonic
I minimize energy E(f) = f>∆fI convex optimization
I closed form solution λ = −(R>∆uuR
)−1R>∆ulYl
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Harmonic mixtures
−2.5 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 2.5−2.5
−2
−1.5
−1
−0.5
0
0.5
1
1.5
2
2.5
λ follows the graph, so does f
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Harmonic mixturescomputational savings
• harmonic mixtures fu = −R(R>∆uuR︸ ︷︷ ︸M×M
)−1R>∆ulYl
• original harmonic function fu = −(∆uu︸︷︷︸u×u
)−1∆ulYl
• harmonic mixtures O(M3) O(u3)
Harmonic mixtures can handle large problems.
Also induction f(x) =∑M
m=1Rxmλm
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Too little labeled data?(Zhu, Lafferty and Ghahramani, 2003b)
• choose unlabeled points to label: active learning
• most ambiguous vs. most influential
01
a 0.5
B 0.4
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Active Learning
• generalization error
err =∑i∈U
∑yi=0,1
(sgn(fi) 6= yi)Ptrue(yi)
• approximation: Ptrue(yi = 1) ≈ fi
• estimated error: err =∑
i∈U min (fi, 1− fi)
• estimated error after querying k with answer yk:
err+(xk,yk) =
∑i∈U
min(f
+(xk,yk)i , 1− f+(xk,yk)
i
)
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Active Learning
• approximation: err+xk ≈ (1− fk)err
+(xk,0) + fkerr+(xk,1)
• select the query k∗ = arg mink err+xk
• ‘re-train’ is fast with harmonic function
f+(xk,yk)u = fu + (yk − fk)
(∆uu)−1·k
(∆uu)−1kk
Active learning can be effectively integrated into semi-supervised
learning.
28
Beyond harmonic functions
• harmonic functions too specialized?
• I will show you things behind harmonic functions:
I probabilistic model
I kernel
• I will give you an even better kernel.
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The probabilistic model behind harmonic function
• energy E(f) =∑
i∼j Wij(fi − fj)2 = f>∆f
• low energy ⇒ good label propagation
E(f) = 4 E(f) = 2 E(f) = 1
• Markov random field p(f) ∝ exp (−E(f))
• if f ∈ 0, 1 discrete, Boltzmann machines, inference hard
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The probabilistic model behind harmonic functionGaussian random fields(Zhu, Ghahramani and Lafferty, ICML2003)
• continuous relaxation f ∈ R
• Gaussian random field p(f) ∝ exp(−f>∆f
)I n-dimensional Gaussian
I harmonic function = mean, easy to compute
I inverse covariance matrix ∆
• equivalent to Gaussian processes on finite data
I with kernel (covariance) matrix ∆−1
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The kernel matrix behind harmonic functions
K = ∆−1
• Kij = indirect similarity
I The direct similarity Wij may be small
I But Kij will be large if many paths between i, j
• K can be used with many kernel machines
I K built with both labeled and unlabeled data
I K + SVM = semi-supervised SVM
I different from transductive SVM (Joachims 1999)
I additional benefit: handles noisy labeled data
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K encourages smooth functions
• graph spectrum ∆ =∑n
k=1 λkφkφ>k
• small eigenvalue, smooth eigenvector∑i∼j Wij(φk(i)− φk(j))2 = λk
• K encourage smooth eigenvectors by large weights
Laplacian ∆ =∑
k λkφkφ>k
harmonic kernel K = ∆−1 =∑
k1λkφkφ
>k
• smooth functions have small RKHS norm
||f ||K = f>K−1f = f>∆f =∑i∼j
Wij(fi − fj)2
33
General semi-supervised kernels
• K = ∆−1 may not be the best semi-supervised kernel
• general semi-supervised kernels
K =∑
i r(λi)φiφ>i
• r(λ) large when λ small, to encourage smooth eigenvectors.
• Specific choices of r() lead to some known kernels
I harmonic function kernel r(λ) = 1/λI diffusion kernel r(λ) = exp(−σ2λ)I random walk kernel r(λ) = (α− λ)p
• Is there a best r()? Yes, as measured by kernel alignment.
34
Alignment measures kernel quality
• alignment between kernel and the labeled data Yl
align(K,Yl) =〈Kll, YlYl
>〉‖ Kll ‖ · ‖ YlYl
> ‖
• extension of cosine angle between vectors
• high alignment related to good generalization performance
• leads to a convex optimization problem
35
Finding the best kernel(Zhu, Kandola, Ghahramani and Lafferty, NIPS2004)
• the order constrained semi-supervised kernel
max r align(K,Yl)subject to K =
∑i riφiφ
>i
r1 ≥ · · · ≥ rn ≥ 0
• order constraints r1 ≥ · · · ≥ rn encourage smoothness
• convex optimization
• r nonparametric
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The order constrained kernel improvesalignment and accuracy
text categorization (religion vs. atheism), 50 labeled and 2000
unlabeled articles.
• alignment
kernel order harmonic RBF
alignment 0.31 0.17 0.04
• accuracy with support vector machines
kernel order harmonic RBF
accuracy 84.5 80.4 69.3
We now have good kernels for semi-supervised learning.
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Sequences and other structured data(Lafferty, Zhu and Liu, 2004)
y’
x’
y
x
• what if x1 · · ·xn form sequences?
speech, natural language
processing, biosequence analysis,
etc.
• conditional random fields (CRF)
• kernel CRF
• kernel CRF + semi-supervised
kernels
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Related work in semi-supervised learning
based on different assumptions
method assumptions references
graph-based similar feature, same label this talk
mincuts (Blum & Chawla, 2001)
normalized Laplacian (Zhou et al., 2003)
regularization (Belkin et al., 2004)
mixture model, EM generative model (Nigam et al., 2000)
transductive SVM low density separation (Joachims, 1999)
info. regularization y varies less when p(x) high (Szummer & Jaakkola 02)
co-training feature split (Blum & Mitchell, 1998)
· · ·
39
Key contributions of the thesis
• use unlabeled data? harmonic functions
• good graph? graph hyperparameter learning
• large problems? harmonic mixtures
• pick items to label? active semi-supervised learning
• probabilistic framework? Gaussian random fields
• kernel machines? semi-supervised kernels
• structured data? kernel conditional random fields
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Summary
Unlabeled data can improve classification.
The methods have reached the stage where we canapply them to real-world tasks.
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Thank You
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Open questions
• more efficient graph learning
• more research on structured data
• regression, ranking, clustering
• new semi-supervised assumptions
• applications: NLP, bioinformatics, vision, etc.
• learning theory: consistency etc.
43
References
M. Belkin, I. Matveeva and P. Niyogi. Regularization and Semi-supervisedLearning on Large Graphs. COLT 2004.A. Blum and S. Chawla. Learning from Labeled and Unlabeled Data usingGraph Mincuts. ICML 2001.A. Blum, T. Mitchell. Combining Labeled and Unlabeled Data with Co-training.COLT 1998.O. Delalleau, Y. Bengio, N. Le Roux. Efficient Non-Parametric FunctionInduction in Semi-Supervised Learning. AISTAT 2005.R. Hwa. A Continuum of Bootstrapping Methods for Parsing Natural Languages.2003.T. Joachims, Transductive inference for text classification using support vectormachines. ICML 1999.K. Nigam, A. McCallum, S Thrun, T. Mitchell. Text Classification from Labeledand Unlabeled Documents using EM. Machine Learning. 2000.M. Szummer, T. Jaakkola. Information Regularization with Partially LabeledData. NIPS 2002.D. Zhou, O. Bousquet, T.N. Lal, J. Weston, B. Schlkopf. Learning with Localand Global Consistency. NIPS 2003.
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X. Zhu, J. Lafferty. Harmonic Mixtures. 2005. submitted.X. Zhu, Jaz Kandola, Z. Ghahramani, J. Lafferty. Nonparametric Transforms ofGraph Kernels for Semi-Supervised Learning. NIPS 2004.J. Lafferty, X. Zhu, Y. Liu. Kernel Conditional Random Fields: Representationand Clique Selection. ICML 2004.X. Zhu, Z. Ghahramani, J. Lafferty. Semi-Supervised Learning Using GaussianFields and Harmonic Functions. ICML 2003.X. Zhu, J. Lafferty, Z. Ghahramani. Combining Active Learning and Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. ICML2003 workshop.X. Zhu, Z. Ghahramani. Learning from Labeled and Unlabeled Data with LabelPropagation. CMU-CALD-02-106, 2002.
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Graph spectrum ∆ =∑n
i=1 λiφiφ>i
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Learning component label with EM
−2.5 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 2.5−2.5
−2
−1.5
−1
−0.5
0
0.5
1
1.5
2
2.5
Labels for the components do not follow the graph.(Nigam et al., 2000)
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KCRF: Sequences and other structured data
• KCRF: pf(y|x) = Z−1(x, f) exp (∑
c f(x,yc))
• f induces regularized negative log loss on training data
R(f) =l∑
i=1
− log pf(y(i)|x(i)) + Ω(‖ f ‖K)
• representer theorem for KCRFs: loss minimizer
f?(x,yc) =l∑
i=1
∑c′
∑y′
c′
α(i,y′c′)K((x(i),y′c′), (x,yc))
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KCRF: Sequences and other structured data
• learn α to minimize R(f), convex, sparse training
• special case K((x(i),y′c′), (x,yc)) = ψ(K ′(x(i)c′ ,xc),y′c′,yc)
• K ′ can be a semi-supervised kernel.
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A random walk interpretationof harmonic functions
• harmonic function fi = P (hit label 1 | start from i)
I random walk from node i to j with probability Pij
I stop if we hit a labeled node
• indirect similarity: random walks have similar destinations
1
0
i
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An image and its neighbors in the graph
image 4005 neighbor 1: time edge neighbor 2: color edge
neighbor 3: color edge neighbor 4: color edge neighbor 5: face edge
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